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by EOS Intelligence EOS Intelligence No Comments

Digital Therapeutics: The Future of Healthcare?

Although the COVID-19 pandemic seems to be done with its rampage, many people still opt to access all kinds of services, including healthcare, from the comfort of their homes. As this trend is expected to continue, the global digital therapeutics market, with its projected growth at a 20% CAGR from 2022 to 2035, is one important sector healthcare firms should focus on right now.

Digital therapeutics (DTx) are digital health interventions or software applications that are clinically validated and designed to treat or manage medical conditions. They can be used alone or in conjunction with traditional medical treatments.

The Digital Therapeutics Alliance categorizes DTx products into three types: disease treatment, disease management, and health improvement.

Examples of DTx include a solution to manage chronic musculoskeletal pain developed by Kaia Health, a biotechnology company in New York. This motion analysis tool assesses and guides patients’ progress during physical therapy and tailors treatment to individual requirements.

Similarly, Clickotine from Click Therapeutics, a company also based in New York, uses AI to help people with nicotine addiction. This solution offers a personalized plan fully integrated with eight weeks of nicotine replacement therapy, including options such as gum, patches, or lozenges. It tracks critical aspects such as daily cigarette counts, craving triggers, craving times, etc. A trial study conducted by the company in 2016 claimed that 45% of Clickotine users were able to quit smoking.

Adoption of DTx is taking off amid increased investments

The commercial development of DTx started around 2015 and, since then, has grown into a global market of considerable size. The total value of global DTx start-ups was estimated at a whopping US$31 billion in 2022, according to a 2022 report published by Dealroom, an Amsterdam-based firm offering data and insights about start-ups and tech ecosystems, in partnership with MTIP (a Swiss-based private equity firm), Inkef (an Amsterdam-based early-stage venture investment firm), and Speedinvest (an Austrian early-stage investor).

The number of people using DTx solutions is expected to increase over the next few years, according to a 2022 report by Juniper Research, a UK-based research firm. The study found that there were 7 million DTx users in the USA in 2020, a number expected to rise to around 40 million in 2026.

This increase can be attributed to the fact that DTx solutions are highly accessible and distributable due to an increase in the use of smartphones. A 2021 report published by Pew Research Center, a US-based think tank, found that 87% of Americans owned a smartphone in 2021, compared to 35% in 2011. With this, more people will be able to access medical care without having to spend more on hospital visits.

DTx applications have also been attracting numerous investors owing to the applications’ cost-effectiveness, ease of distribution, and better accessibility. According to the same 2022 report published by Dealroom, global venture capital funding in DTx witnessed a fourfold increase in 2022 compared to 2017.

All these studies reveal that, despite certain challenges, the DTx applications hold the promise of developing into a practical and affordable means of treating illnesses and conditions that impact large numbers of people.

Regulatory pitfalls present a major roadblock to DTx adoption

One main challenge DTx companies face is the regulatory environment. All DTx products must comply with the regulations of regional agencies such as the FDA, HIPAA, HITECH, etc.

Many US firms initially faced regulatory obstacles and payer resistance around product reimbursement. Before 2017, the US FDA classified DTx solutions as a SaMD (Software as a Medical Device) and, therefore, made them subject to risk assessment (low, medium, or high). Due to this, DTx solutions needed premarket approval and rigorous clinical trial results to get approval.

This has improved with the introduction of the Digital Health Innovation Action Plan by the FDA in 2017. According to the new plan, the FDA will first consider the company producing the solution. If the producer has demonstrated quality and excellence, it can market lower-risk devices with a streamlined premarket review. Post-market surveillance and data collection are also done to evaluate product efficiency.

Similarly, in the EU, DTx is controlled by national competent authorities and governed by the European Regulation on Medical Devices 2017/745 (MDR). However, no specific framework indicates the evidence required for assessing the performance or quality of DTx solutions or their production standards. This means that the member states may interpret the dossier requirements differently, leading to a fractured regulatory environment.

The COVID-19 pandemic has provided companies with some regulatory flexibility, leading to an increase in venture capital funding. In 2020, the federal government in the USA issued a new rule allowing healthcare practitioners to treat patients across state lines, including the use of digital medicine. This can increase access to healthcare, especially in rural areas, and physicians will be able to offer timely care to their patients traveling in a different state.

The FDA has also loosened regulations during COVID-19, particularly for mental health products, with the Digital Health Innovation Action Plan. This was to ensure that patients received timely care even from their homes while reducing the burden on hospitals. It waived certain regulatory obligations, such as the need to file a 510(k) premarket notification during the COVID-19 pandemic. The 510(k) is a submission indicating that a new medical device is similar to something already approved by the FDA (a predicate device) to ensure safety and efficiency. However, finding suitable comparables can be highly challenging in the case of DTx, which is dynamically evolving. This can result in misunderstandings or overlooking of critical aspects of these solutions, leading to uncertainty and delays in the approval process. The waiver of this regulation offers DTx companies some relief in the future.

Digital Therapeutics - The Future of Healthcare by EOS Intelligence

Digital Therapeutics – The Future of Healthcare by EOS Intelligence

Patient health literacy is a hurdle in the adoption of DTx solutions

A survey by the National Assessment of Adult Literacy (NAAL) in 2003 has shown that only 12% of Americans possess proficient health literacy skills, making them able to find and understand information related to their health. This lack of awareness among patients can also impede the ease of applying DTx products.

Patient experience is also crucial for the acceleration of DTx adoption. Older patients unfamiliar with using technological gadgets can find it difficult to adopt DTx solutions. However, a 2022 AMA survey has shown that 90% of people over the age of 50 in the USA recognize some benefit from digital health tools.

Similarly, a survey conducted by the Pew Research Center in 2021 indicated an increase in the use of smartphones and the internet among older people in the USA, driven by the pandemic. Older adults are using technological applications for activities such as entertainment, banking, shopping, etc., even after the pandemic, a 2021 survey by AARP Research, a US-based NPO, shows. This indicates that there is scope for an increase in adoption.

Many companies are now trying to increase patient involvement by using gamification, aiming at patient groups for whom DTx use is likely to be more challenging (e.g., older population, children). DTx developers include game-like elements or mechanics into a DTx solution, such as tasks, rewards, badges, points, and leaderboards. An example is US-based Akili Interactive’s EndeavorRx, a prescription DTx aimed at enhancing attention function in children with ADHD aged 8 to 12. It uses an interactive mobile video game to assist children in improving their attention skills and adjusting to their performance levels. The game’s sensory stimuli and motor challenges also help kids multitask and tune out distractions.

Payer reluctance affects many DTx products

Although the number of DTX products on the market increases, payers’ reluctance to cover their costs to the patient can also slow down adoption. The coverage of DTx solutions is limited, even when they are FDA-approved. Only 25% of payers are currently willing to cover prescription DTx solutions, according to a 2022 survey by MMIT, a Pennsylvania-based market data provider, which involved 16 payers.

Akili Interactive’s EndeavorRx is one such solution facing insurance coverage issues. Elevance Health (previously Anthem) denied coverage for EndeavorRx, deeming it medically unnecessary, while Aetna, another insurance provider, considers it experimental and investigational.

A study released by Health Affairs, a health policy research journal, in November 2023 has shown that only two of the twenty FDA-approved prescription DTx solutions on the market have undergone rigorous evidence-based evaluation. This means that no authoritative results indicating the benefits of these solutions for various population demographics are available, making many payers skeptical of their medical claims.

DTx offers solutions for managing multiple conditions

Over the past few years, several prominent players have emerged in the DTx landscape. Around 59% of the DTx market is concentrated in the North American region and 28% in Europe.

Top players, such as Akili Interactive and Big Health, both US-based firms, focus on offering products for managing mental health illnesses, mostly management of anxiety, depression, and stress, according to a report published in 2023 (based on data until September 2022) by Roots Analysis, an India-based pharma/biotech market research firm. With about 970 million people suffering from mental health conditions globally (according to the WHO), the potential user pool is enormous, offering growth opportunities for DTx solutions developed to address mental illnesses and, over time, driving the growth of the DTx market as a whole.

Many top companies also focus on solutions offering pain management and treatment for chronic conditions such as diabetes, obstructive pulmonary disease, and musculoskeletal disorders. An example is US-based Omada’s pain management solution, Omada MSK. This application guides patients through various customized exercises and records their movements, which are then assessed by a licensed physical therapist (PT), who can make recommendations for improvement. It also has a tool that utilizes computer vision technology to help PTs virtually assess a patient’s movement and range of motion, allowing them to make necessary changes in the therapy.

Similarly, several DTx solutions on the market now focus specifically on diabetes, which affects around 537 million adults globally. Some top companies focus on the previously unmet needs of conventional methods, such as weight management or preventing prediabetes, to help with overall diabetes treatment. US-based Omada’s solution, Omada Prediabetes, comes with a weight scale pre-connected to the app, and the weight is added to the app as soon as the patient steps on the scale. A dedicated health coach assesses the patient’s weight, creates a customized plan, and monitors the patient’s progress. In other similar DTx solutions for diabetes, an app can also give insulin dose recommendations based on the patient’s blood glucose levels.

DTx can serve in a range of other conditions, including major depressive disorder, autism spectrum disorder, and multiple sclerosis, to name a few.

The DTx landscape is rife with development

The DTx business landscape has recently seen many developments, from acquisitions to product launches. One of them was Big Health’s acquisition of Limbix, a California-based DTx firm, in July 2023 to bolster its portfolio, including SparkRx, a treatment for adolescents dealing with depression and anxiety. Similarly, in June 2023, Kaia Health launched Angela, a HIPAA-compliant, AI-powered voice-based digital care assistant, to serve as a companion and guide, enhancing the physical therapy experience for patients.

In another development, BehaVR, a DTx company headquartered in Kentucky, and Fern Health, a digital chronic pain management program, merged their companies in November 2023 to create a novel pain management DTx solution that addresses both pain and fear caused by chronic diseases. With this merger, they launched RealizedCare, an app designed to offer a comprehensive solution that collaborates with health plans, employers, and value-based providers to treat a range of behavioral and mental health conditions. This solution provides clinicians with immersive programs specifically designed for in-clinic use. It is initially focusing on chronic pain.

Bankruptcy of Pear and lessons for the industry

However, the most shocking development in the DTx market was the bankruptcy of Pear Therapeutics in 2023. The remains of this once-prominent company were purchased by four other companies for a total of US$6.05 million at an auction. Pear was a big name in the industry since its inception in 2013. It introduced numerous products such as reSET, reSET-O, and Somryst for treating substance use disorder, opioid use disorder, and chronic insomnia, respectively. It was also the first company to receive FDA approval for a mobile app aimed at treating substance use disorders.

Though the company announced layoffs of nearly 20% of its workforce in November 2022, its management expressed optimism about the company’s growth and reduced operating expenses in the third quarter. But in April 2023, the company filed for bankruptcy.

The demise of Pear has opened the eyes of industry experts to the challenges faced by DTx players. Certain issues were unique to Pear itself, such as the comparatively higher prices of its products and the focus on treating challenging conditions such as substance use disorders. However, the bankruptcy of Pear also brings attention to the obstacles that can be faced by any other DTx company. One crucial roadblock is that physicians and payers still approach these products with caution. Additionally, achieving profitability for DTx might be challenging for all types of players, particularly for small start-ups lacking substantial market influence. The bankruptcy of Pear and the challenges it faced can be used by budding DTx companies as a road map as they navigate this complex sector.

EOS Perspective

DTx is all set to revolutionize the medical industry, with a 2020 McKinsey report suggesting it could potentially alleviate the global disease burden by up to 10% by 2040. Given the impact of emerging treatments on stakeholders, pharmaceutical and healthcare companies should consider expanding their portfolio to include DTx solutions.

With telehealth companies seeing good growth in the pandemic and post-pandemic years, an increase in investment can be expected as they are uniquely placed to support prescription DTx. With the growth of the digital health industry, prominent telehealth providers may also choose to acquire DTx businesses or create their own in-house DTx solutions.


Read our related Perspective:
 COVID-19 Outbreak Boosts the Use of Telehealth Services

An increase in industry M&A activities can be expected in the next few years, with growing incidences of chronic illnesses, improved technology penetration across all age groups, and a maturing market. Big names such as Bayer, Novartis, and Sanofi are also entering into partnerships with DTx companies, indicating a bright future for the sector.

Mental health and behavioral therapy are great fields to branch out for companies starting in the DTx landscape, especially in this post-pandemic era. Demand for such services is likely to be sustained, considering the National Institute of Mental Health Disorders estimates that one in four adults in the USA suffers from a diagnosable mental illness, with many suffering from multiple conditions.

Similarly, diseases such as diabetes, cancer, heart, and respiratory ailments are on the rise. Healthcare companies can effectively address these medical areas through the use of DTx applications, providing personalized care for patients. This approach has the potential to manage not only chronic conditions such as diabetes but also terminal illnesses such as cancer.

Many DTx players will likely focus on areas with unmet needs, including pediatrics and metabolic disorders. With seven DTx-based diabetic management solutions already receiving 510(k) clearance as of December 2022, it can be expected that more products addressing the treatment gaps might flood the market.

The DTx industry is gradually maturing and has been receiving significant investments in recent years (US$8 billion in 2022). While experts view it as a profitable market, hesitation remains, particularly following the bankruptcy of Pear Therapeutics.

Nevertheless, due to the COVID-19 pandemic and subsequent lockdown measures, technology adoption among older adults has increased significantly. Hence, strategic investments in DTx by pharmaceutical and healthcare companies, taking into account market conditions, can expect to establish a stronger presence in this industry in the future.

by EOS Intelligence EOS Intelligence No Comments

Commentary: Europe AI Regulation Deal – Beginning of a New Technological Era?

The proliferation of artificial intelligence (AI) applications in recent years has highlighted the importance of regulatory frameworks to ensure AI’s responsible use. Recognizing this, the EU has become the first global power to pass AI-related legislation. The EU AI Act, considered a watershed moment in today’s digital era, is expected to create ripples worldwide and prompt leaders to take initiatives to control the use of AI.

The AI industry, valued at US$454.1 billion in 2022, is predicted to reach US$2.6 trillion by 2032, according to a 2023 report by Canada/India-based market research company Precedence Research. Although this impressive increase in the use of AI offers immense potential, it has raised numerous concerns about its misuse. Many industry experts have voiced concern about how significantly AI impacts important industries such as education and health, and may eventually alter human lives.

Regulatory bodies and governments worldwide are also now aware of the risks of bias, discrimination, and privacy breaches that come with the unrestricted use of AI. A 2020 study published in Sustainable Development, an interdisciplinary journal, found that unchecked AI poses a threat to the Sustainable Development Goals (SDGs) established by the UN.

The EU took its first step in addressing concerns related to AI in April 2021 when it released the first draft of EU AI regulation. However, this draft was revised after the 2022 release of ChatGPT to include the newer technological advances with a future-proof approach that will enable the law to evolve as technology does.

The EU AI rule uses a risk-based strategy to divide AI systems into categories, namely unacceptable, high, limited, and minimal risk. Systems categorized in the unacceptable risk group will be banned, and those with high risk will undergo a rigorous assessment to understand their effect on fundamental rights and be given a CE mark before their market release.

The limited risk category that fulfills specific transparency requirements should follow EU copyright laws, prepare technical documentation, and release a synopsis of the training materials so the users can make an informed decision. Companies can release minimal-risk systems, such as spam filters and AI-powered video games, without restrictions.

The AI Act has also introduced certain transparency requirements for general-purpose AI (GPAI) models such as Gemini and ChatGPT. For powerful and highly effective models, there are additional risk management requirements, such as maintaining cybersecurity standards, conducting evaluations, assessing and mitigating risks, and reporting serious events.

The EU AI Act has several implications for business across the continent

Many industry experts consider the EU AI Act a significant regulatory tool for overseeing the advancement and utilization of AI technologies throughout the continent. The enforcement of this act is expected to influence significantly the operations, approaches, and competitive environment across several sectors in the EU, as well as intercontinental business with products traded in the European market.

Achieving compliance is one of the main challenges businesses will face with the introduction of the new EU AI law. The law comes with a hefty penalty for non-compliance, with most violations costing businesses €15 million, or 3% of their annual global turnover. Non-compliance concerning banned AI systems can result in fines of up to €35 million, or 7% of the company’s annual turnover. Furthermore, providing false, deceptive, or incomplete information may result in fines of up to €7.5 million, or 1.5% of the total turnover.

Any organization aiming for compliance needs to perform a gap analysis to identify disparities and enhance company processes, policies, and metrics. They must also provide the regulators with accurate, efficient, and timely answers. All these place a substantial organizational and economic burden on the companies.

Another challenge businesses can face is implementing all the changes needed to fill the compliance gap while being consistent with their internal structure. This will require the company to identify the metrics it needs to track to achieve compliance and design new organizational procedures to fulfill this. This should also be done in such a way that it does not hinder other strategic goals, such as innovation, budgetary constraints, etc., placing additional strain on the leadership.

Companies offering multiple AI solutions can face several complicated roadblocks with the implementation of the EU AI Act. Such organizations will be subject to a different set of regulations depending on the risk category of their AI products. This can lead to internal policy confusion.

Slower product development cycles and delays in product releases are other bottlenecks that companies will need to address with the act’s implementation. New AI-powered products, especially high-risk ones, now need to undergo more rigorous evaluation to ensure compliance, which can slow the entire process.

Players can also face challenges in M&A activities with the new regulations. Businesses will now need to spend more time and resources assessing the compliance of their merging partner before proceeding.

There are several opportunities for determined businesses

While the implementation of the EU AI Act does spell several challenges for businesses, it also offers opportunities for interested players. With the new act in place, customers will be able to place more trust in AI solutions. This will enhance the adoption of new AI-based systems.

Determined players can also improve innovation and gain investment with the help of Article 53 of the Act. This section states the possibility of establishing “regulatory sandboxes” that can be set up by one or more member states. These sandboxes offer controlled environments for developing, testing, and validating new AI technologies for a short time under the guidance of a competent authority. This will also ensure that the AI solutions fulfill regulatory compliance.

The EU AI Act offers special support measures to start-ups and SMEs. The compliance requirements for high-risk AI have been modified to make them less burdensome and technically feasible. Start-ups and SMEs also get a proportional cap on compliance infringement fines. This will make it easier for budding businesses in the AI sector to make leaps in innovation and product development.

Interested EU-based players can also receive support for product development through programs such as Testing and Experimentation Facilities, Data and Robotics Hubs, Digital Innovation Hubs, etc. The AI Office, set up by the EU AI Act to oversee the rules and regulations related to GPAI models, has established forums where stakeholders can exchange best practices and contribute to rules of conduct and practice. This can improve participation across industries and enhance inclusiveness.

EOS Perspective

The EU AI Act can be considered a landmark development in the regulation of AI technologies in Europe. It has extensive implications for businesses, society, and the economy on the continent and worldwide. Many industry leaders consider this act a starting point in AI regulation. Other countries are expected to follow in the EU’s footsteps soon and make similar laws curbing the effects of unregulated AI.

The EU AI Act is expected to make AI-based solutions safe and bias-free, with better transparency into their developmental processes. It is also expected to enhance accountability and create a more responsible approach to AI development and deployment.

Businesses, especially SMEs and start-ups, can expect several benefits from this act. Experts predict that with the renewed focus on safety and ethical issues, there will be large-scale development of far more trustworthy and robust AI-based solutions in the future.

by EOS Intelligence EOS Intelligence No Comments

Scarcity Breeds Innovation – The Rising Adoption of Health Tech in Africa

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Africa carries the world’s highest burden of disease and experiences a severe shortage of healthcare workers. Across the continent, accessibility to primary healthcare remains to be a major challenge. During the COVID-19 pandemic, several health tech companies emerged and offered new possibilities for improving healthcare access. Among these, telemedicine and drug distribution services were able to address the shortage of health workers and healthcare facilities across many countries. New health tech solutions such as remote health monitoring, hospital automation, and virtual health assistance that are backed by AI, IoT, and predictive analytics are proving to further improve health systems in terms of costs, access, and workload on health workers. Given the diversity in per capita income, infrastructure, and policies among African countries, it remains to be seen if health tech companies can overcome these challenges and expand their reach across the continent.

Africa is the second most populated continent with a population of 1.4 billion, growing three times faster than the global average. Amid the high population growth, Africa suffers from a high prevalence of diseases. Infectious diseases such as malaria and respiratory infections contribute to 80% of the total infectious disease burden, which indicates the sum of morbidity and mortality in the world. Non-communicable diseases such as cancer and diabetes accounted for about 50% of total deaths in 2022. High rates of urbanization also pose the threat of spreading communicable diseases such as COVID-19, Ebola, and monkey fever.

A region where healthcare must be well-accessible is indeed ill-equipped due to limited healthcare infrastructure and the shortage of healthcare workers. According to WHO, the average doctor-to-population ratio in Africa is about two doctors to 10,000 people, compared with 35.5 doctors to 10,000 people in the USA.

Poor infrastructure and lack of investments worsen the health systems. Healthcare expenditure (aggregate public healthcare spending) in African countries is 20-25 times lower than the healthcare expenditure in European countries. Governments here typically spend about 5% of GDP on healthcare, compared with 10% of GDP spent by European countries. Private investment in Africa is less than 25% of the total healthcare investments.

Further, healthcare infrastructure is unevenly distributed. Professional healthcare services are concentrated in urban areas, leaving 56% of the rural population unable to access proper healthcare. There are severe gaps in the number of healthcare units, diagnostic centers, and the supply of medical devices and drugs. Countries such as Zambia, Malawi, and Angola are placed below the rank of 180 among 190 countries ranked by the WHO in terms of health systems. Low spending power and poor national health insurance schemes discourage people from using healthcare services.

Health tech solutions’ potential to fill the healthcare system gaps

As the prevailing health systems are inadequate, there is a strong need for digital solutions to address these gaps. Health tech solutions can significantly improve the access to healthcare services (consultation, diagnosis, and treatment) and supply of medical devices and drugs.

Health tech solutions can significantly improve the access to healthcare services (consultation, diagnosis, and treatment) and supply of medical devices and drugs.

For instance, Mobihealth, a UK-based digital health platform founded in 2017, is revolutionizing access to healthcare across Africa through its telemedicine app, which connects patients to over 100,000 physicians from various parts of the world for video consultations. The app has significantly (by over 60%) reduced hospital congestion.

Another example is the use of drones in Malawi to monitor mosquito breeding grounds and deliver urgent medical supplies. This project, which was introduced by UNICEF in 2017, has helped to curb the spread of malaria, which typically affects the people living in such areas at least 2-3 times a year.

MomConnect, a platform launched in 2014 by the Department of Health in South Africa, is helping millions of expectant mothers by providing essential information through a digital health desk.

While these are some of the pioneers in the health-tech industry, new companies such as Zuri Health, a telemedicine company founded in Kenya in 2020, and Ingress Healthcare, a doctor appointment booking platform launched in South Africa in 2019, are also strengthening the healthcare sector. A study published by WHO in 2020 indicated that telemedicine could reduce mortality rates by about 30% in Africa.

The rapid rise of health tech transforming the African healthcare landscape

Digital health solutions started to emerge during the late 2000’s in Africa. Wisepill, a South African smart pill box manufacturing company established in 2007, is one of the earliest African health tech success stories. The company developed smart storage containers that alert users on their mobile devices when they forget to take their medication. The product is widely used in South Africa and Uganda.

The industry gained momentum during the COVID-19 pandemic, with the emergence of several health tech companies offering remote health services. The market experienced about 300% increase in demand for remote healthcare services such as telemedicine, health monitoring, and medicine distribution.

According to WHO, the COVID pandemic resulted in the development of over 120 health tech innovations in Africa. Some of the health tech start-ups that emerged during the pandemic include Zuri Health (Kenya), Waspito (Cameroon), and Ilara Health (Kenya). Several established companies also developed specific solutions to tackle the spread of COVID-19 and increase their user base. For instance, Redbird, a Ghanaian health monitoring company founded in 2018, gained user attention by launching a COVID-19 symptom tracker during the pandemic. The company continues to provide remote health monitoring services for other ailments, such as diabetes and hypertension, which require regular health check-ups. Patients can visit the nearest pharmacy instead of a far-away hospital to conduct tests, and results will be regularly updated on their platform to track changes.

Scarcity Breeds Innovation – The Rising Adoption of Health Tech in Africa by EOS Intelligence

Start-ups offering advanced solutions based on AI and IoT have been also emerging successfully in recent years. For instance, Ilara Health, a Kenya-based company, founded during the COVID-19 pandemic, is providing affordable diagnostic services to rural population using AI-powered diagnostic devices.

With growing internet penetration (40% across Africa as of 2022) and a rise in investments, tech entrepreneurs are now able to develop solutions and expand their reach. For instance, mPharma, a Ghana-based pharmacy stock management company founded in 2013, is improving medicine supply by making prescription drugs easily accessible and affordable across nine countries in Africa. The company raised a US$35 million investment in January 2022 and is building a network of pharmacies and virtual clinics across the continent.

Currently, 42 out of 54 African countries have national eHealth strategies to support digital health initiatives. However, the maximum number of health tech companies are concentrated in countries such as South Africa, Nigeria, Egypt, and Kenya, which have the highest per capita pharma spending in the continent. Nigeria and South Africa jointly account for 46% of health tech start-ups in Africa. Telemedicine is the most offered service by start-ups founded in the past five years, especially during the COVID-19 pandemic. Some of the most popular telemedicine start-ups include Babylon Health (Rwanda), Vezeeta (Egypt), DRO Health (Nigeria), and Zuri Health (Kenya).

Other most offered services include medicine distribution, hospital/pharmacy management, and online booking and appointments. Medicine distribution start-ups have an immense impact on minimizing the prevalence of counterfeit medication by offering tech-enabled alternatives to sourcing medication from open drug markets. Many physical retail pharmacy chains, such as Goodlife Pharmacy (Kenya), HealthPlus (Nigeria), and MedPlus (Nigeria), are launching online pharmacy operations leveraging their established logistics infrastructure. Hospitals are increasingly adopting automation tools to streamline their operations. Electronic Medical Record (EMR) management tools offered by Helium Health, a provider of hospital automation tools based in Nigeria are widely adopted in six African countries.

Medicine distribution start-ups have an immense impact on minimizing the prevalence of counterfeit medication by offering tech-enabled alternatives to sourcing medication from open drug markets.

For any start-up in Africa, the key to success is to provide scalable, affordable, and accessible digital health solutions. Low-cost subscription plans offered by Mobihealth (a UK-based telehealth company founded in 2018) and Cardo Health (a Sweden-based telehealth company founded in 2021) are at least 50% more affordable than the average doctor consultation fee of US$25 in Africa. Telemedicine platforms such as Reliance HMO (Nigeria) and Rocket Health (Uganda) offer affordable health insurance that covers all medical expenses. Some governments have also taken initiatives in partnering with health tech companies to provide affordable healthcare to their people. For instance, the Rwandan government partnered with a digital health platform called Babylon Health in 2018 to deliver low-cost healthcare to the population of Rwanda. Babylon Health is able to reach the majority of the population through simple SMS codes.

Government support and Public-Private Partnerships (PPPs)

With a mission to have a digital-first universal primary care (a nationwide program that provides primary care through digital tools), the Rwandan government is setting an example by collaborating with Babylon Health, a telemedicine service that offers online consultations, appointments, and treatments.

As part of nationwide digitization efforts, the government has established broadband infrastructure that reaches 90% population of the country. Apart from this, the country has a robust health insurance named Mutuelle de Santé, which reaches more than 90% of the population. In December 2022, the government of Ghana launched a nationwide e-pharmacy platform to regulate and support digital pharmacies. Similarly, in Uganda, the government implemented a national e-health policy that recognizes the potential of technology in the healthcare sector.

MomConnect, a mobile initiative launched by the South African government with the support of Johnson and Johnson in 2014 for educating expectant and new mothers, is another example of a successful PPP. However, apart from a few countries in the region, there are not enough initiatives undertaken by the governments to improve health systems.

Private and foreign investments

In 2021, health tech start-ups in Africa raised US$392 million. The sustainability of investments became a concern when the investments dropped to US$189 million in 2022 amid the global decline in start-up funding.

However, experts predict that the investment flow will improve in 2023. Recently, in March 2023, South African e-health startup Envisionit Deep AI raised US$1.65 million from New GX Ventures SA, a South African-based venture capital company. Nigerian e-health company, Famasi, is also amongst the start-ups that raised investments during the first quarter of 2023. The company offers doorstep delivery of medicines and flexible payment plans for medicine bills.

The companies that have raised investments in recent years offer mostly telemedicine and distribution services and are based in South Africa, Nigeria, Egypt, and Kenya. That being said, start-ups in the space of wearable devices, AI, and IoT are also gaining the attention of investors. Vitls, a South African-based wearable device developer, raised US$1.3 million in funding in November 2022.

Africa-based incubators and accelerators, such as Villgro, The Baobab Network, and GrowthAfrica Accelerator, are also supporting e-health start-ups with funding and technical guidance. Villgro has launched a US$30 million fund for health tech start-ups in March 2023. Google has also committed US$4 million to fund health tech start-ups in Africa in 2023.

Digital future for healthcare in Africa

There were over 1,700 health tech start-ups in Africa as of January 2023, compared with about 1,200 start-ups in 2020. The rapid emergence of health tech companies is addressing long-running challenges of health systems and are offering tailored solutions to meet the specific needs of the African market.

Mobile penetration is higher than internet penetration, and health tech companies are encouraged to use SMS messaging to promote healthcare access. However, Africa is expected to have at least 65% internet penetration by 2025. With growing awareness of the benefits of health tech solutions, tech companies would be able to address new markets, especially in rural areas.

Companies that offer new technologies such as AI chatbots, drones, wearable devices for remote patient monitoring, hospital automation systems, e-learning platforms for health workers, the Internet of Medical Things (IoMT), and predictive analytics are expected to gain more attention in the coming years. Digitally enabled, locally-led innovations will have a huge impact on tackling the availability, affordability, and quality of health products and services.

Digitally enabled, locally-led innovations will have a huge impact on tackling the availability, affordability, and quality of health products and services.

Challenges faced by the health tech sector  

While the African health tech industry has significantly evolved over the last few years, there are still significant challenges with regard to infrastructure, computer literacy, costs, and adaptability.

For instance, in Africa, only private hospitals have switched to digital records. Many hospitals still operate without computer systems or internet connections. About 40% of the population are internet users, with countries such as Nigeria, Egypt, South Africa, Morocco, Ghana, Kenya, and Algeria being the ones with the highest number of internet users (60-80% of the population). However, 23 countries in Africa still have low internet penetration (less than 25%). This is the major reason why tech companies concentrate in the continent’s largest tech hubs.

On the other hand, the majority of the rural population prefers face-to-face contact due to the lack of digital literacy. Electricity and internet connectivity are yet to reach all parts of the region and the cost of the internet is a burden for many people. Low-spending power is a challenge, as people refuse to undergo medical treatment due to a lack of insurance schemes to cover their medical expenses. Insurance schemes provided in Africa only cover 60% of their healthcare expenses. Even though health tech solutions bring medical costs down, these services still remain unaffordable for people in low-income countries. Therefore, start-ups do not prefer to establish or expand their services in such regions.

Another hurdle tech companies face is the diversity of languages in Africa. Africa is home to one-third of the world’s languages and has over 1,000 languages. This makes it difficult for companies to customize content to reach all populations.

Amidst all these challenges, there is very little support from the governments. The companies face unfavorable policies and regulations that hinder the implementation of digital solutions. Only 8% of African countries have online pharmacy regulations. In Nigeria, regulatory guidelines for online pharmacies only came into effect in January 2022, and there are still unresolved concerns around its implementation.

Lack of public investment and comprehensive government support also discourage the local players. Public initiatives are rare in providing funding, research support, and regulatory approval for technology innovations in the health sector. Private investment flow is low for start-ups in this sector compared to other industries. Health tech start-ups raised a total investment of US$189 million in 2022, which is not even 10% of the total investments raised by start-ups in other sectors in Africa. Also, funding is favored towards the ones established in high-income countries. Founders who don’t have ties to high-income countries struggle to raise funds.

EOS Perspective

The emergence of tech health can be referred to as a necessary rise to deal with perennial gaps in the African healthcare system. Undoubtedly, many of these successful companies could transform the health sector, making quality health services available to the mass population. The pandemic has spurred the adoption of digital health, and the trend experienced during the pandemic continues to grow with the developments in the use of advanced technologies such as AI and IoT. Telemedicine and distribution have been the fastest-growing sectors driven by the demand for remote healthcare services during the pandemic. Home-based care is likely to keep gaining momentum with the development of advanced solutions for remote health monitoring and diagnostic services.

Home-based care is likely to keep gaining momentum with the development of advanced solutions for remote health monitoring and diagnostic services.

With the increasing internet penetration and acceptance of digital healthcare, health tech companies are likely to be able to expand their reach to rural areas. Right policies, PPPs, and infrastructure development are expected to catalyze the health tech adoption in Africa. Companies that offer advanced technologies such as IoT-enabled integrated medical devices, AI chatbots, drones, wearable devices for remote patient monitoring, hospital automation systems, e-learning platforms for health workers, and predictive analytics for health monitoring are expected to emerge successfully in the coming years.

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Is ChatGPT Just Another Tech Innovation or A Game Changer?

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ChatGPT, a revolutionary AI-based conversational chatbot, has been making headlines around the world. The AI-based tool can answer user queries and generate new content in a human-like way. By automating tasks such as customer support and content creation, ChatGPT has the potential to revolutionize many industries, resulting in a more efficient digital landscape and an enhanced user experience. However, the technology is not without its risks and poses a number of issues, such as creating malicious content, copyright infringement, and other moral issues. Despite these challenges, the possibilities for ChatGPT are infinite, and with the advancement of technology, the opportunities it presents will only continue to expand.

ChatGPT is an AI-based question-and-answer chatbot that responds to user queries in a conversational way, just like how humans respond. OpenAI, a US-based research and development company, launched ChatGPT in November 2022. Since then, ChatGPT has garnered increased attention and popularity worldwide. The tool surpassed over 1 million users within five days and 100 million users within two months of launch.

ChatGPT has become popular due to its capability to answer queries in a simple and conversational manner. The tool can perform various functions, such as generating content for marketing campaigns, writing emails, blogs, and essays, debugging code, and even solving mathematics questions.

OpenAI’s ChatGPT works on the concept of generative AI and uses a language model called GPT3 – a third-generation Generative Pre-trained Transformer. The AI chatbot has been fed with about 45 terabytes of text data on a diverse range of topics from sources such as books, websites, and articles and has been trained on a set of algorithms to understand relationships between words and phrases and how it is used in context. This way, the model is able to develop an understanding of languages and generate answers. ChatGPT uses a dialog format, asks follow-up questions for clarification, admits mistakes, and is capable of dismissing inappropriate or dangerous requests.

ChatGPT also has a simple user interface, allowing communication through a plain textbox just like a messaging app, thus making it easy to use. Currently, ChatGPT is in beta testing, and users can use it for free to try and provide feedback. However, the free version is often inaccessible and out of capacity due to the increasing traffic.

In February 2023, OpenAI launched a pilot subscription plan named ChatGPT Plus, starting at US$20 per month, which is available to its customers in the USA. The subscription plan provides access to ChatGPT even during peak times and provides prior access to any new features. OpenAI is also testing ChatGPT to generate videos and pictures using its DALLE image-generating software, which is another AI tool developed by OpenAI to create art and images from text prompts. OpenAI also plans to launch a ChatGPT mobile app soon.

How could ChatGPT help businesses?

One of the most impactful areas where ChatGPT can make a difference is customer support. The AI tool can handle a large volume of consumer queries within a short time frame and give accurate responses, which can boost work efficiency and reduce employees’ workload.

In addition, the tool can also be employed to answer sales-related queries. By training ChatGPT to understand product information, pricing, and other details, businesses can provide a seamless sales experience for customers. ChatGPT can also analyze user data and behavior and can assist customers to find the products they are looking for, and give product recommendations leading to a more tailored and enjoyable shopping experience. ChatGPT can be incorporated into websites to engage visitors and help them find the information they need, which can help in lead generation.

Another potential benefit of ChatGPT is its ability to automate content generation. ChatGPT can generate unique and original content quickly, making it an effective tool for creating marketing materials such as email campaigns, blogs, newsletters, etc.

ChatGPT could be used in a number of industries, such as travel, education, real estate, healthcare, information technology, etc. For instance, in the tech industry, ChatGPT can write programs in specific programming languages such as JavaScript, Python, and React, and can be very helpful to developers in generating code snippets and for code debugging.

In healthcare, the tool can be used in scheduling appointments, summarizing patient’s health information based on previous history, assisting in diagnostics, and for telemedicine services.

In the education sector, ChatGPT can be used to prepare teaching materials and lessons and to provide personalized tutoring classes.

These are just a few applications of ChatGPT. As generative technology continues to evolve, there may be many other potential applications that can help businesses achieve their goals more efficiently and effectively.

Is ChatGPT Just Another Tech Innovation or A Game Changer by EOS Intelligence

ChatGPT’s output may not be always accurate

While ChatGPT offers several benefits and advantages, the tool is not without limitations. ChatGPT works on pre-trained data that cannot handle nuances or other ambiguities and thus may generate answers that are incorrect, biased, or inappropriate.

Moreover, ChatGPT is not connected to the internet and cannot refer to an external link to respond to queries that are not part of its training. It also does not cover the news and events after 2021 and cannot provide real-time information.

Another major limitation is that the tool is often out of capacity due to the high traffic, which makes it inaccessible. There are also other potential risks associated with these generative AI tools. Some of the threats include writing phishing emails, copyright infringement, generating abusive content or malicious software, plagiarism, and much more.

ChatGPT is not the first or only AI chatbot

While ChatGPT has garnered most of the attention in the last few months, it is neither the first nor the only AI-based chatbot in the market. There are many AI-based writers and AI chatbots in the market. These tools vary in their applications and have their own strengths and weaknesses.

For instance, ChatSonic, first released in 2020, is an AI writing assistant touted as the top ChatGPT alternative. This AI chatbot is supported by Google, has voice dictation capabilities, can generate up-to-date content, and can also generate images based on text prompts. However, ChatSonic has word limits in its free as well as paid versions, which makes it difficult for users who need to generate large pieces of text.

Similarly, Jasper is another AI tool launched in 2021, which works based on the language model (GPT-3) similar to ChatGPT. Jasper can write and generate content for blogs, videos, Twitter threads, etc., in over 50 language templates and can also check for grammar and plagiarism. Jasper AI is specifically built for dealing with business use cases and is also faster and more efficient and generates more accurate results than ChatGPT.

YouChat is another example, developed in 2022 by You.com, and running on OpenAI GPT-3. It performs similar functions as ChatGPT – responding to queries, solving math equations, coding, translating, and writing content. This chatbot cites source links of the information and acts more like an AI-powered search engine. However, YouChat lacks an aesthetic appeal and may generate results that are outdated at times.

ChatGPT-styled chatbots to power search engines

While a lot of buzz has been created about this technology, the impact of AI-based conversational chatbots is yet to be seen on a large scale. Many proclaim that tools such as ChatGPT will replace the traditional search method of using Google to obtain information.

However, experts argue that it is highly unlikely. While AI chatbots can mimic human-like conversation, they need to be trained on massive amounts of data to generate any kind of answers. These tools work on pre-trained models that were fed with large amounts of data sourced from books, articles, websites, and many more resources to generate content. Hence, real-time learning and answering would be cost-intensive in the long run.

Moreover, ChatGPT’s answers may not always be comprehensive or accurate, requiring human supervision. ChatGPT may also not be very good at solving logical questions. For instance, when asked to solve a simple problem – “RQP, ONM, _, IHG, FED, find the missing letters”, ChatGPT answered incorrectly as “LKI”. Similarly, when provided a text prompt, “The odd numbers in the group 17, 32, 3, 15, 82, 9, 1 add up to an even number”, the chatbot affirmed it, which is false. Moreover, the AI chatbot does not cover news after 2021, and when asked, “Who won the 2022 World Cup?” ChatGPT said the event has not taken place.

On the other hand, Google uses several algorithms to rank web pages and gives the most relevant web results and comprehensive information. Google has access to a much larger pool of data and the ability to analyze it in real time. Additionally, Google’s ranking algorithms have been developed over years of research and refinement, making them incredibly efficient and effective at delivering high-quality results. Therefore, while AI chatbots can be useful in certain contexts, they are unlikely to replace traditional search methods, such as Google.

However, leading search engines are looking to incorporate ChatGPT into their search tools. For instance, Microsoft is planning to incorporate ChatGPT 4, a faster version of the current ChatGPT version, into its Bing Search engine. Since 2019, the company has invested about US$13 billion in OpenAI, the parent company of ChatGPT.

In February 2023, Microsoft also incorporated ChatGPT into its popular office software Teams. With this, users with Teams premium accounts will able to generate meeting notes, access recommended tasks, and would be able to see personalized highlights of the meeting using ChatGPT. These add immense value to the user.

In February 2023, China-based e-commerce company Alibaba also announced its plan to launch its own AI chatbot similar to ChatGPT. Similarly, Baidu, a China-based internet service provider, launched a chatbot named “Ernie” in its search engine in March 2023.

Amidst the increasing popularity of ChatGPT, Google has also started working on a chatbot named “Bard” based on its own language model, Lambda. The company is planning to launch more than 20 new AI-based products in 2023. In February 2023, Google invested about US$400 million in Anthropic AI, a US-based artificial intelligence startup, which is testing a new chatbot named Claude. Thus, the race to build an effective AI-enabled search engine has just begun, and things have to unfold a bit to learn more about how chatbots can modify web searches.

On the other hand, AI technologies such as ChatGPT are sure to leave an impact on how businesses operate. With the global economy slowing down, resulting in low business margins, many businesses are looking to cut down costs to increase profitability.

ChatGPT could be extremely beneficial to companies looking to automate various business tasks, such as customer support and content generation. The tool can be integrated into channels, including websites and voice assistants. While this sounds beneficial, there is also a likelihood of the technology displacing some jobs such as customer service representatives, copywriters, research analysts, etc.

However, ChatGPT will not be replacing the human workforce completely since many business tasks require creative and critical thinking skills and other traits such as empathy and emotional intelligence that only humans have. This technology is expected to pave the way for new opportunities in various fields, such as software engineering and data analysis, and allow employees to focus on more value-added tasks instead of routine, mundane tasks, ultimately boosting productivity.

EOS Perspective

With their remarkable ability to generate human-like conversations and high-quality content, generative AI tools, such as ChatGPT, are sure to be touted as a game-changer for many businesses. The advancements in generative AI are expected to have a significant impact on various business tasks such as customer support, content creation, data analysis, marketing and sales, and even decision-making.

Investors are slowly taking note of the immense potential the technology holds. It is estimated that generative AI start-ups received equity funding totaling about US$2.6 billion across 110 deals in 2022, which echoes an increasing interest in the technology.

The adoption of generative AI technologies is poised to increase, especially in business processes where a human-like conversation is desirable. Industries such as e-commerce, retail, and travel are likely to embrace this technology to automate customer service tasks, reduce costs, and increase efficiency. In addition, generative AI is likely to become an indispensable part of industries such as finance and logistics, where high levels of accuracy and precision are required. Media and entertainment companies can also benefit from this technology to quickly generate content such as articles, videos, and audio.

That being said, generative AI is not without its risks, and the technology could be used to create fake and other discriminatory information. Hence, there is an inevitable need to ensure that generative AI models are trained and deployed in an ethical and responsible manner. Despite these challenges, there is increased research and significant activity going on in the field of generative AI, especially with regard to combining the capabilities of chatbots and traditional search engines.

The current chatbots will continue to evolve and will lead to the creation of even more advanced and sophisticated models. The popularity of generative AI tools such as ChatGPT is unlikely to wane, and the technology is here to stay, with the potential to create better prospects for business and a brighter future for society.

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Automotive Industry Gearing towards Digital Transformation with AI

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Artificial intelligence (AI) has become an integral part of almost every industry, and the automotive sector is no exception. From self-driving cars to predictive maintenance, AI is evolving as a major disruptor in the auto industry, slowly transforming how automobiles are designed, manufactured, and sold. This digital swing is driven mainly by increased competition, consumer preferences for smart mobility, and the benefits of AI. However, AI adoption in the automotive industry is not mainstream yet, with the technology deployed only at the pilot level and in selective business segments. As the world gears toward an era of digital transformation and automation, AI is expected to be part of various business processes in the automotive industry in the coming years.

Artificial intelligence in the auto industry is typically associated with autonomous and self-driving cars. However, the technology has increasingly found its way into other applications over the last few years. Leading auto OEMs are showing an interest in deploying AI-driven innovations across the value chain, investing in tech start-ups, partnering with software providers, and building new business entities.

For instance, a venture capital fund owned by Japanese automaker Toyota, Toyota AI Ventures (rebranded as Toyota Ventures now), with US$200 million in assets under management, invested in almost 35 early-age startups that focus on AI, autonomy, mobility, and robotics between 2017 and 2020. Similarly, in 2022, South Korean automotive manufacturer Hyundai invested US$424 million to build an AI research center in the USA to advance research in AI and robotics. In the same year, CARIAD, a software division of the Germany-based Volkswagen Group, acquired Paragon Semvox GmbH, a Germany-based company that develops AI-based voice control and smart assistance systems, for US$42 million.

Changing consumer preferences, competitive pressures, and various advantages of AI are driving this transformation. According to a 2019 Capgemini research study, nearly 25% of auto manufacturers in the USA implemented AI solutions at scale, followed by the UK (14%) and Germany (12%) by the end of 2019.

There are numerous applications of AI in the automotive industry. Some of the more common and innovative uses of AI include virtual simulation models, inventory management, quality control of parts and finished goods, automated driver assistance systems (ADAS), predictive maintenance, and personalized vehicles, to name a few.

Automotive Industry Gearing towards Digital Transformation with AI by EOS Intelligence

AI-based virtual simulation models used for effective R&D processes

Due to changing customer preferences, increasing regulations concerning safety and fuel emissions, and technological disruption, OEMs are finding it more expensive to make cars nowadays. A 2020 report by PricewaterhouseCoopers says that conceptualization and product development account for 77% of the cost and 65% of the time spent in a typical automotive manufacturing process.

To make R&D cost-effective and more efficient, some auto manufacturers and tier-I suppliers are turning to AI. AI enables the simulation of digital prototypes, eliminating a lot of physical prototypes, thus reducing the costs and time for product development. One interesting concept that is emerging and catching attention in this area is the “digital twin”. The concept employs a virtual model mimicking an entire process or environment and its physical behavior. There are numerous uses of digital twins – in vehicle design and development, factory and supply chain simulations, autonomous driving simulations, etc. In vehicle design and development, digital twins make simulations easier, validate each step of the development in order to predict outcomes, improve performance, and identify possible failures before the product enters the production line.

For instance, in 2019, Continental, a Germany-based automotive parts manufacturing company, entered into a collaboration with a Germany-based start-up, Automotive Artificial Intelligence (AAI), to develop a modular virtual simulation program for its Automated Driver Assistance System (ADAS) application and also invested an undisclosed amount in the company. The virtual simulation program could generate phenomenal vehicle test data of 5,000 miles per hour compared to 6,500 miles of physical test driving per month, reducing both time and costs.

Many leading automotive companies are also looking to utilize this innovative concept in streamlining the entire manufacturing operations. For example, in early 2023, Mercedes-Benz announced that the company is partnering with Nvidia Technologies, a US-based technology company specializing in AI-based hardware and software, to build a digital twin of one of its automotive plants in Germany. Mercedes-Benz is hoping that the digital twin can help them monitor the entire plant and make quick changes in their production processes without interruptions.

General Motors, Volkswagen, and Hyundai use AI for smart manufacturing

Automation processes and industrial robots have been in automotive manufacturing for a long time. However, these systems can perform only programmed routine and repetitive tasks and cannot act on complex real-life scenarios.

The use of AI in automotive manufacturing makes these production processes smarter and more efficient. Some of the applications of AI in manufacturing include forecasting component failures, predicting demand for components and managing inventory, using collaborative robots for heavy material handling, etc.

For instance, General Motors, a US-based automotive manufacturing company, has been using AI-based design strategies since 2018 to manufacture lightweight vehicles. In 2019, the company also deployed an AI-based image classification tool in its robots to detect equipment failures on pilot-level experimentation.

Similarly, a Germany-based luxury car manufacturer, Audi, has been using AI to monitor the quality of spot welds since 2021 and is also planning to use AI in its wheel design process starting in 2023. In 2021, Audi’s parent company, Volkswagen, also invested about US$1 billion to bring technologies such as cloud-based industrial software, intelligent robotics, and AI into its factory operations. With this, the company aims to drive a 30% increase in manufacturing performance in its plants in the USA and Mexico by 2025.

In another instance, South Korean automotive manufacturer Hyundai uses AI to improve the well-being of its employees. In 2018, the company developed wearable robots for its workers, who spend most of their time in assembly lines. These robots can sense the type of work of employees, adjust their motions, and boost load support and mobility, preventing work-related musculoskeletal disorders. Thus, AI is transforming every facet of automobile manufacturing, from designing to improving the well-being of employees.

Companies provide more ADAS features amidst increasing competition

Automated Driver Assistance System (ADAS) is one of the powerful applications of AI in the automotive industry. ADAS are intelligent systems that aim to make driving safer and more efficient. ADAS primarily uses cameras and Lidar (Light Detection and Ranging) sensors to generate a high-resolution 360-degree view of the car and assists the driver or enables cars to take autonomous actions. Demand for ADAS is growing globally due to consumers’ rising preference for luxury, better safety, and comfort. It is estimated that by 2025, ADAS will become a default feature of nearly every new vehicle sold worldwide. ADAS is classified into 6 levels:

Level 0 No automation
Level 1 Driver assistance: the vehicle has at least a single automation system
Level 2 Partial driving automation: the vehicle has more than one automated system; the driver has to be on alert at all times
Level 3 Conditional driving automation: the vehicle has multiple driver assistance functions that control most driving tasks; the driver has to be present to take over if anything goes wrong
Level 4 High driving automation: the vehicle can make decisions itself in most circumstances; the driver has the option to manually control the car
Level 5 Full driving automation: the vehicle can do everything on its own without the presence of a driver

At present, cars from level 0 to level 2 are on the market. To meet the growing competitive edge, several auto manufacturers are adding more automation features to the level 2 type. Companies have also been making significant strides toward developing autonomous vehicles. For instance, auto manufacturers such as Mercedes, BMW, and Hyundai are testing level 3 autonomous vehicles, and Toyota and Honda are testing and trialing level 4 vehicles. This indicates that the future of mobility will be highly automated relying upon technologies such as AI.

Volkswagen and Porsche use AI in automotive marketing and sales

There are various applications of AI in marketing and sales operations – in sales forecasting and planning, personalized marketing, AI-assisted virtual assistants, etc. According to a May 2022 Boston Consulting Group (BCG) report, auto OEMs can gain faster returns with lower investments by deploying AI in their marketing and sales operations.

Some automotive companies have already started to deploy AI in sales and marketing. For instance, since 2019, Volkswagen has been leveraging AI to create precise market forecasts based on certain variables and uses the data for its sales planning. Similarly, in 2021, a Germany-based luxury car manufacturer, Porsche, launched an AI tool that suggests various vehicle options and their prices based on the customer’s preferences.

Automakers integrate AI-assisted voice assistants into cars

Cars nowadays are not only perceived as a means of transportation, but consumers also expect sophisticated features, convenience, comfort, and an enriching experience during their journey. AI enhances every aspect of the cockpit and deploys personalized infotainment systems that learn from user preferences and habits over time. Many automakers are integrating AI-based voice assistants to help drivers navigate through traffic, change the temperature, make calls, play their favorite music, and more.

For instance, in 2018, Mercedes-Benz introduced the Mercedes Benz User Experience (MBUX) voice-assisted infotainment system, which gets activated with the keyword “Hey Mercedes”. Amazon, Apple, and Google are also planning to get carmakers to integrate their technologies into in-car infotainment systems. It is expected that 90% of new vehicles sold globally will have voice assistants by 2028.

Integration and technological challenges hamper the adoption of AI

The adoption of AI in the automotive industry is still at a nascent stage. Several OEM manufacturers in the automotive industry are leveraging various AI solutions only at the pilot level, and scaling up is slow due to the various challenges associated with AI.

At the technology level, the creation of AI algorithms remains the main challenge, requiring extensive training of neural networks that rely on large data sets. Organizations lack the skills and expertise in AI-related tools to successfully build and test AI models, which is time-consuming and expensive. AI technology also uses a variety of high-priced advanced sensors and microprocessors, thus hindering the technology from being economically feasible.

Moreover, AI acts more or less like a black box, and it remains difficult to determine how AI models make decisions. This obscurity remains a big problem, especially for autonomous vehicles.

At the organizational level, integration challenges make it difficult to implement the technology with existing infrastructure, tools, and systems. Lack of knowledge of selecting and investing in the right AI application and lack of information on potential economic returns are other biggest organizational hurdles.

EOS Perspective

The applications of AI in the automotive industry are broad, and many are yet to be envisioned. There has been an upswing in the number of automotive AI patents since 2015, with an average of 3,700 patents granted every year. It is evident that many disrupting high-value automotive applications of AI are likely to be deployed in the coming decade. Automotive organizations are bolstering their AI skills and capabilities by investing in AI-led start-ups. These companies together already invested about US$11.2 billion in these startups from 2014 to 2019.

There is also an increase in the hiring pattern of AI-related roles in the industry. Many automotive industry leaders are optimistic that AI technology can bring significant economic and operational benefits to their businesses. AI can turn out to be a powerful steering wheel to drive growth in the industry. The future of many industries will be digital, and so will be for the automotive sector. Hence, for automotive businesses that are yet to make strides toward this digital transformation, it is better to get into this trend before it gets too late to keep up with the competition.

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Powering Healthcare Diagnostics with AI: a Pipe Dream or Reality

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The growing paucity of radiologists across the globe is alarming. The availability of radiologists is extremely disproportionate globally. To illustrate this, Massachusetts General Hospital in Boston, USA, had 126 radiologists, while the entire country of Liberia had two radiologists, and 14 countries in the African continent did not have a single radiologist, as of 2015. This leads to a crucial question – how to address this global unmet demand for radiologists and diagnostic professionals?

Increasing capital investment signals rising interest in AI in healthcare diagnostics

The global market for Artificial Intelligence (AI) in healthcare diagnostics is forecast to grow at a CAGR of 8.3%, from US$513.3 million in 2019 to US$825.9 million in 2025, according to Frost & Sullivan’s report from 2021. This growth in the healthcare diagnostics AI market is attributed to the increased demand for diagnostic tests due to the rising prevalence of novel diseases and fast-track approvals from regulatory authorities to use AI-powered technologies for preliminary diagnosis.

Imaging Diagnostics, also known as Medical Imaging is one of the key areas of healthcare diagnostics that is most interesting in exploring AI implementation. From 2013 to 2018, over 70 firms in the imaging diagnostics AI sector secured equity funding spanning 119 investment deals and have progressed towards commercial beginnings, thanks to quick approvals from respective regulatory bodies.

Between 2015 and 2021, US$3.5 billion was secured by AI-enabled imaging diagnostics firms (specialized in developing AI-powered solutions) globally for 290 investment deals, as per Signify Research. More than 200 firms (specialized in developing AI-powered solutions) globally were building AI-based solutions for imaging diagnostics, between 2015 and 2021.

The value of global investments in imaging diagnostics AI in 2020 was approximately 8.8% of the global investments in healthcare AI. The corresponding figure in 2019 was 10.2%. The sector is seeing considerable investment at a global level, with Asia-based firms (specialized in developing AI-powered solutions) having secured around US$1.5 billion, Americas-based companies raising US$1.2 billion, and EMEA-based firms securing over US$600 million between 2015 and 2021.

As per a survey conducted by the American College of Radiology in 2020 involving 1,427 US-based radiologists, 30% of respondents said that they used AI in some form in their clinical practice. This might seem like a meager adoption rate of AI amongst US radiologists. However, considering that five years earlier, there were hardly any radiologists in the USA using AI in their clinical practice, the figure illustrates a considerable surge in AI adoption here.

However, the adoption of AI in healthcare diagnostics is faced with several challenges such as high implementation costs, lack of high-quality diagnostic data, data privacy issues, patient safety, cybersecurity concerns, fear of job replacement, and trust issues. The question that remains is whether these challenges are considerable enough to hinder the widespread implementation of AI in healthcare diagnostics.

Powering Healthcare Diagnostics with AIPowering Healthcare Diagnostics with AI

AI advantages help answer the needs in healthcare diagnostics

Several advantages such as improved correctness in disease detection and diagnosis, reduced scope of medical and diagnosis errors, improved access to diagnosis in areas where radiologists are unavailable, and increased workflow and efficacy drive the surge in the demand for AI-powered solutions in healthcare diagnostics.

One of the biggest benefits of AI in healthcare diagnostics is improved correctness in disease detection and diagnosis. According to a 2017 study conducted by two radiologists from the Thomas Jefferson University Hospital, AI could detect lesions caused by tuberculosis in chest X-rays with an accuracy rate of 96%. Beth Israel Deaconess Medical Center in Boston, Massachusetts uses AI to scan images and detect blood diseases with a 95% accuracy rate. There are numerous similar pieces of evidence supporting the AI’s ability to offer improved levels of correctness in disease detection and diagnosis.

A major benefit offered by AI in healthcare diagnostics is the reduced scope of medical and diagnosis errors. Medical and diagnosis errors are among the top 10 causes of death globally, according to WHO. Taking this into consideration, minimizing medical errors with the help of AI is one of the most promising benefits of diagnostics AI. AI is capable of cutting medical and diagnosis errors by 30% to 40% (trimming down the treatment costs by 50%), according to Frost & Sullivan’s report from 2016. With the implementation of AI, diagnostic errors can be reduced by 50% in the next five years starting from 2021, according to Suchi Saria, Founder and CEO, Bayesian Health and Director, Machine Learning and Healthcare Lab, Johns Hopkins University.

Another benefit that has been noticed is improved access to diagnosis in areas where there is a shortage of radiologists and other diagnostic professionals. The paucity of radiologists is a global trend. To cite a few examples, there is one radiologist for: 31,707 people in Mexico (2017), 14,634 people in Japan (2012), 130,000 people in India (2014), 6,827 people in the USA (2021), 15,665 people in the UK (2020).

AI has the ability to modify the way radiologists operate. It could change their active approach toward diagnosis to a proactive approach. To elucidate this, instead of just examining the particular condition for which the patient requested medical intervention, AI is likely to enable radiologists to find other conditions that remain undiagnosed or even conditions the patient is unaware of. In a post-COVID-19 era, AI is likely to reduce the backlogs in low-emergency situations. Thus, the technology can help bridge the gap created due to radiologist shortage and improve the access to diagnosis of patients to a drastic extent.

Further, AI helps in improving the workflow and efficacy of healthcare diagnostic processes. On average at any point in time, more than 300,000 medical images are waiting to be read by a radiologist in the UK for more than 30 days. The use of AI will enable radiologists to focus on identifying dangerous conditions rather than spend more time verifying non-disease conditions. Thus, the use of AI will help minimize such delays in anomaly detection in medical images and improve workflow and efficacy levels. To illustrate this, an AI algorithm named CheXNeXt, developed in a Stanford University study in 2018 could read chest X-rays for 14 distinct pathologies. Not only could the algorithm achieve the same level of precision as the radiologists, but it could also read the images in less than two minutes while the radiologists could read them in an average of four hours.

Black-box AI: A source of challenges to AI implementation in healthcare diagnostics

The black-box nature of AI means that with most AI-powered tools, only the input and output are visible but the innards between them are not visible or knowable. The root cause of many challenges for AI implementation in healthcare diagnostics is AI’s innate character of the black box.

One of the primary impediments is tracking and evaluating the decision-making process of the AI system in case of a negative result or outcome of AI algorithms. That is to say, it is not possible to detect the fundamental cause of the negative outcome within the AI system because of the black-box nature of AI. Therefore, it becomes difficult to avoid such occurrences of negative outcomes in the future.

The second encumbrance caused by the black-box nature of AI is the trust issues of clinicians that are hesitant to use AI applications because they do not completely comprehend the technology. Patients are also expected to not have faith in the AI tools because they are less forgiving of machine errors as opposed to human errors.

Further, several financial, technological, and psychological challenges while implementing AI in healthcare diagnostics are also associated with the black-box nature of the technology.

Financial challenges

High implementation costs

According to a 2020 survey conducted by Definitive Healthcare, a leading player in healthcare commercial intelligence, cost continues to be the most prominent encumbrance in AI implementation in diagnostics. Approximately 55% of the respondents who do not use AI pointed out that cost is the biggest challenge in AI implementation.

The cost of a bespoke AI system can be between US$20,000 to US$1 million, as per Analytics Insights, while the cost of the minimum viable product (a product with sufficient features to lure early adopters and verify a product idea ahead of time in the product development cycle) can be between US$8,000 and US$15,000. Other factors that also decide the total cost of AI are the costs of hiring and training skilled labor. The cost of data scientists and engineers ranges from US$550 to US$1,100 per day depending on their skills and experience levels, while the cost of a software engineer (to develop applications, dashboards, etc.) ranges between US$600 and US$1,500 per day.

It can be gauged from these figures that the total cost of AI implementation is high enough for the stakeholders to ponder upon the decision of whether to adopt the technology, especially if they are not fully aware of the benefits it might bring and if they are working with ongoing budget constraints, not infrequent in healthcare institutions.

Technological challenges

Overall paucity of availability of high-quality diagnostic data

High-quality diagnostic and medical datasets are a prerequisite for the testing of AI models. Because of the highly disintegrated nature of medical and diagnostic data, it becomes extremely difficult for data scientists to procure the data for testing AI algorithms. To put it in simple terms, patient records and diagnostic images are fragmented across myriad electronic health records (EHRs) and software platforms which makes it hard for the AI developer to use the data.

Data privacy concerns

AI developers must be open about the quality of the data used and any limitations of the software being employed, without risking cybersecurity and without breaching intellectual property concerns. Large-scale implementation of AI will lead to higher vulnerability of the existing cloud or on-premise infrastructure to both physical and cyber attacks leading to security breaches of critical healthcare diagnostic information. Targets in this space such as diagnostic tools and medical devices can be compromised by malware or software viruses. Compromised data and algorithms will result in errors in diagnosis and consequently inaccurate recommendations of treatment thereby causing stakeholders to refrain from using AI in healthcare diagnostics.

Patient safety

One of the foremost challenges for AI in healthcare diagnostics is patient safety. To achieve better patient safety, developers of AI algorithms must ensure the credibility, rationality, and transparency of the underlying datasets. Patient safety depends on the performance of AI which in turn depends on the quality of the training data. The better the quality of the data, the better will be the performance of the AI algorithms resulting in higher patient safety.

Mental and psychological challenges

Fear of job substitution

A survey published in March 2021 by European Radiology, the official journal of the European Society of Radiology, involving 1,041 respondents (83% of them were based in European countries) found that 38% of residents and radiologists are worried about their jobs being cut by AI. However, 48% of the respondents were more enterprising and unbiased towards AI. The fear of substitution could be attributed to the fact that those having restricted knowledge of AI are not completely educated about its shortcomings and consider their skillset to be less up-to-date than the technology. Because of this lack of awareness, they fail to realize that radiologists are instrumental in developing, testing, and implementing AI into clinical practice.

Trust issues

Trusting AI systems is crucial for the profitable implementation of AI into diagnostic practice. It is of foremost importance that the patient is made aware of the data processing and open dialogues must be encouraged to foster trust. Openness or transparency that forges confidence and reliability among patients and clinicians is instrumental in the success of AI in clinical practice.

EOS Perspective

With trust in AI amongst clinicians and patients, its adoption in healthcare diagnostics can be achieved at a more rapid pace. Lack of it breeds fear of job replacement by the technology amongst clinicians. Further, scarcity of awareness of AI’s true potential as well as its limitations also threatens diagnostic professionals from getting replaced by the technology. Therefore, to fully understand the capabilities of AI in healthcare diagnostics, clinicians and patients must learn about and trust the technology.

With the multitude and variety of challenges for AI implementation in healthcare diagnostics, its importance in technology becomes all the more critical. The benefits of AI are likely to accelerate the pace of adoption and thereby realize the true potential of AI in terms of saving clinicians’ time by streamlining how they operate, improving diagnosis, minimizing errors, maximizing efficacy, reducing redundancies, and delivering reliable diagnostic results. To power healthcare diagnostics with AI, it is important to view AI as an opportunity rather than a threat. This in turn will set AI in diagnostics on its path from pipe dream to reality.

by EOS Intelligence EOS Intelligence No Comments

Beauty Tech Giving Beauty Industry a Facelift

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In recent years, artificial intelligence and virtual reality have been adding an additional dimension to the beauty industry, quite literally. With consumers increasingly embracing and demanding personalized offerings and precise results, leading brands, such as L’Oréal and Shiseido are investing heavily in the space. Just as in many other industries, AI is revolutionizing beauty products and how they are conceptualized, created, and sold. However, it is a long road from being perceived as gimmicky promotions to improving customer engagement to becoming commercial go-to solutions.

Artificial intelligence (AI) has been greatly integrated in our lives through different sectors and now the beauty industry is no exception. The use of AI, augmented reality (AR), virtual reality (VR) as well as complex beauty devices has revolutionized the way consumers perceive, apply, and select beauty products. Moreover, in the age of online retail, it enables companies to maintain a similar personalized level of service that would otherwise require a physical interaction with a beauty consultant. Technology is creating new experiences for the consumer, both in terms of beauty products’ features as well as purchasing process.

Beauty industry is also one of the most competitive sectors, with consumers always being on the lookout for new products and having low brand loyalty. Beauty tech seems to address this issue as well, as it elevates consumer engagement through enhanced personalized offerings, which in turn is a trend that has been driving the beauty industry for several years now.

The three main aspects of beauty tech encompass personalization through AI, virtual makeup using AR and VR, and smart skincare tools/beauty gadgets.

Personalization through AI

Across the retail sector, the key to consumer’s heart and pockets for a long time has been personalization of products and sales experience. Beauty industry is no exception. Consumers have been looking for the perfect skincare product that work best for them or the lipstick shade that goes perfectly with their skin tone. Moreover, consumers want this all from the comfort of their home. This is where AI comes in.

Through retail kiosks and mobile apps, AI enables companies to offer personalized shade offerings that are especially curated for the individual user. A number of companies is investing and capitalizing on this technology to differentiate themselves in the eyes of the consumer. One of the leading market players in the beauty industry, L’Oréal, has been one of the first companies to invest in AI- and VR-based beauty tech and acquired Toronto-based, ModiFace, in 2018. There are several different ways companies, such as L’Oréal, have incorporated AI into their product offerings.

Beauty Tech Giving Beauty Industry a Facelift by EOS Intelligence

Beauty Tech Giving Beauty Industry a Facelift by EOS Intelligence

Lancôme (a subsidiary of L’Oréal) has placed an AI-powered machine, called Le Teint Particulier, at Harrods and Selfridges in the UK, which creates custom-made foundation for the customer. The machine first identifies ones facial color using a handheld scanner, post which it uses a proprietary algorithm to select a foundation shade from 20,000 combinations. Following this, the machine creates the personalized shade for the user, which can then be bottled and purchased.

In addition to physical store solutions, AI-powered apps and websites also offer consumers personalized recommendations. In 2019, L’Oréal applied ModiFace’s AI technology to introduce a new digital skin diagnostic tool, called SkinConsult, for its brand, Vichy. The AI-powered tool uses more than 6,000 clinical images in order to deliver accurate skin assessment for all skin types. It analyzes selfies uploaded by users to identify fine lines, dark spots, wrinkles, and other issues, and then provides tailored product and routine recommendations to the user to address the skin concerns.

My Beauty Matches, a UK-based company, offers AI-based personalized and impartial beauty product recommendations and price comparisons. The website asks consumers diagnostic-style questions about their skin and hair type, concerns, and preferences, and uses AI to analyze the data and recommend products from 400,000 products (from about 3,500 brands) listed on its website. Alongside, the company runs Beauty Matches Engine (BME), which is a solution for beauty retailers using consumer data and AI algorithms to identify consumer purchasing and browsing patterns as well as their preferred products by age and skin or hair concerns. This helps retailers predict and stock, which product the consumer is likely to purchase, improving sales, increasing upsells, and providing a personalized solution to customers.

On similar lines, another app, Reflexion, uses AI to measure the shininess of skin through pictures and offers personalized product recommendations. The app claims to provide much deeper analysis than regular image analysis apps and provides additional features such as testing if products such as foundation are evenly applied. The app works by measuring a face surface’s Bidirectional Scatter Distribution Function (BSDF), which is a measure of light reflected on the user’s face.

Nudemeter is another such product, which uses AI to personalize makeup choices and foundation shades for a full spectrum of skin tones, including darker skins. The app uses color analysis and digital image processing along with its AI algorithms that ensure accurate color measurement irrespective of background lighting, pixels, etc. The app is currently being used by Spktrm Beauty, a US-based niche beauty company targeting shoppers with dark skin.

Virtual makeup through AR and VR

In today’s world where consumers prefer to shop from the comfort of their homes, AR and VR are enabling beauty companies to provide experience similar to that of physical retail to their consumers. AR and VR technologies-based apps let users experiment virtually with a range of cosmetics by allowing them to try several different shades, all within minutes and through their smartphone. This elevates the users shopping experience and improves sales conversion.

Sephora’s Virtual Makeup Artist enables customers to try on thousands of shades of lipsticks and eyeshadows through their smartphones or at kiosks at Sephora stores. While many such apps and filters have been in use for some time now, they are increasingly becoming more sophisticated, providing accurate color match to the skin and ensuring the virtual makeup does not move when the user shakes their face, changes to a side angle, etc. In addition, such apps also provide digital makeup tutorials to engage customers.

On similar lines, L’Oréal uses ModiFace’s AR and AI technology to provide virtual makeup try-on on Amazon and Facebook. The technology enables customers using these two platforms to try on different shades of lipsticks and other make-up products through a live video or a selfie from an array of L’Oréal brands such as Maybelline, L’Oréal Paris, NYX Professional Makeup, Lancôme, Giorgio Armani, Yves Saint Laurent, Urban Decay, and Shu Uemura.

Moreover, AR-based try-on apps helped brands connect with their customers during the previous year when most customers were stuck home and could not physically try on make-up. LVMH-owned Benefit Cosmetics has been investing in AR tech, and launched Benefit’s Brow Try-On Experience program (along with Taiwanese beauty-tech company, Perfect Corporation), which helps online shoppers identify the right eyebrow shape and style for them and then choose products accordingly. The company uses facial point detector technology for the program. The app witnessed a 43% surge in its daily users during April and May of 2020 (as compared with January and March 2020), when people were confined to their homes owing to the COVID outbreak. This helped connect with consumers in a fresh manner and increased brand loyalty. Moreover, Benefit claims that brows products have been their strongest category post-COVID outbreak.

One of China’s leading e-commerce players, Alibaba, also partnered with Perfect Corporation to integrate the latter’s ‘YouCam Makeup’ (an AR-based virtual makeup try-on technology) into Alibaba’s Taobao and Tmall online shopping experience.

Smart devices

In addition to AI and AR based apps and solutions, smart devices is another category in the beauty tech space that is gaining momentum. A certain section of premium consumers are increasingly open to invest heavily into smart beauty gadgets that not only improve skin and hair quality but also help them quantitatively measure the results from using a certain product. While these products are currently expensive and for a niche audience, they have been gaining popularity, especially across the USA and China.

One such smart skincare device is L’Oréal’s Perso, which is based on ModiFace’s AI-powered skin diagnostics and analysis technology. Perso uses AI, location data, and consumer preferences to formulate personalized moisturizer for the consumer. The product is further expected to extend into foundations and lip shades. Perso is expected to be launched in 2021.

On similar lines, in July 2019, Japan-based Shiseido, launched its smart skincare device called Optune, which measures a user’s location-based weather and air pollution data, sleep data, stress levels, and menstrual cycles to create a custom moisturizer. Optune is available on a subscription basis and costs about US$92 per month.

In 2020, P&G also launched a premium skincare system, called Opte Precision. The skincare device uses blue LED light to scan one’s skin and applies a patented precision algorithm to detect problem areas and analyze complexion. Post this, the device releases an optimizing serum that is applied to spots to instantly cover age spots, pigmentation, etc., and to fade their appearance over time. The device has 120 nozzles and works on a technology similar to that of a thermal inkjet printer. The device targets a premium niche audience and costs US$599 with refill cartridge costing US$100.

In 2018, Johnson & Johnson’s drugstore skincare brand, Neutrogena, also launched a smart skincare device – a skin scanner, called Skin360 and SkinScanner, which uses technology from FitSkin (a US-based technology company). The scanner comes in the form of a magnifying camera that gets attached to a smartphone. The camera, which has a 30-time magnifying power helps scan the size and appearance of one’s pores, size and depth of fine lines and wrinkles, the skin’s moisture level, and also provides a score to the skin’s hydration level. The data is processed in a mobile app, which in turn provides a complete skin analysis and offers expert advice and product recommendations. While most smart skin devices are relatively expensive, this one retails at around US$50.

EOS Perspective

While AI and AR have been embraced by a lot of industries in the past, beauty tech is still in its infancy. That being said, there is a lot of potential in the space, especially with the consumer becoming increasingly comfortable with technology. While till recently, most technology-based products in the beauty sector were gimmicky and more for fun and consumer engagement, brands have started taking this space seriously, and started launching products that offer real sales growth opportunity.

Moreover, while AI and AR-based technologies have been accepted fairly easily by the consumers and industry players alike, smart devices is still a very niche category, with most products focused on a niche affluent clientele, who are willing to spend more than US$100 on products that may help improve their skin. There is a lot of potential for this segment to innovate, collaborate, and launch products at a more affordable price point in order to reach the masses.

Over the next couple of years, we can expect new niche players, exploring the benefits of beauty tech to enter the market in addition to greater number of partnerships between traditional beauty giants and technology companies. As personalization continues to be the mantra for consumers, beauty companies cannot look to ignore the space in the coming future.

by EOS Intelligence EOS Intelligence No Comments

Agritech in Africa: How Blockchain Can Help Revolutionize Agriculture

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In the first part of our series on agritech in Africa, we took a look into how IT and other technology investments are helping small farmers in Africa. In the second part, we are exploring the impact that potential application of advanced technologies such as blockchain can have on the African agriculture sector.

Blockchain, or distributed ledger technology, is already finding utility across several business sectors including financial, banking, retail, automotive, and aviation industries (click here to read our previous Perspectives on blockchain technology). The technology is finding its way in agriculture too, and has the potential to revolutionize the way farming is done.


This article is the second part of a two-piece coverage focusing on technological advancements in agriculture across the African continent.

Read part one here: Agritech in Africa: Cultivating Opportunities for ICT in Agriculture


State of blockchain implementation in agriculture in Africa

Agricultural sector in Africa has already witnessed the onset of blockchain based solutions being introduced in the market. Existing tech players and emerging start-ups have developed blockchain solutions, such as eMarketplaces, agricultural credit/financing platforms, and crop insurance services. Companies, globally as well as within Africa, are harnessing applications of blockchain to develop innovative solutions targeted at key stakeholders across the food value chain.

Blockchain to promote transparency across agriculture sector

The most common application of blockchain in any industry sector (and not only agriculture) is creating an immutable record of transactions or events, which is particularly helpful in creating a trusted record of land ownership for farmers, who are traditionally dependent on senior village officials to prove their ownership of land.

Since 2017, a Kenyan start-up, Land LayBy has been using an Ethereum-based shared ledger to keep records of land transactions. This offers farmers a trusted and transparent medium to establish land ownership, which can then further be used to obtain credit from banks or alternative financing companies. BanQu and BitLand are other examples of blockchain being used as a proof of land ownership.

This feature of blockchain also enables creation of a transparent environment where companies can trace the production and journey of agricultural products across their supply chain. Transparency across the supply chain helps create trust between farmers and buyers, and the improved visibility of prices further down the value chain also enables farmers to get better value for their produce.

In 2017, US-based Bext360 started a pilot project with US-based Coda Coffee and its Uganda-based coffee export partner, ​​Great​ ​Lakes​ ​Coffee. The company developed a machine to grade and weigh coffee beans deposited to Great Lakes by individual farmers in East Uganda. The device uploads the data on a blockchain-based SaaS solution, which enables users to trace the coffee from its origin to end consumer. The blockchain solution is also used to make payments to the farmers based on the grade of their produce in form of tokens.

In 2017, Amsterdam-based Moyee Coffee also partnered with KrypC, a global blockchain, to create a fully blockchain-traceable coffee. The coffee beans are sourced from individual farmers in Ethiopia, and then roasted within the country, before being exported to the Netherlands.

This transparency can help food companies to isolate the cause of any disease outbreak impacting the food value chain. This also allows consumers can be aware of the source of the ingredients used in their food products.

Agritech in Africa: How Blockchain Can Help Revolutionize Agriculture by EOS Intelligence

Blockchain-based platforms to improve farmer and buyer collaboration

Blockchain can also act as a platform to connect farmers with vendors, food processing, and packaging companies, providing a secure and trusted environment to both buyers and suppliers to transact without the need of a middleman. This also results in elimination of margins that need to be paid to these intermediaries, and helps improve the margins for buyers.

Farmshine, a Kenyan start-up, created a blockchain-based platform to auger trade collaboration among farmers, buyers, and service providers in Kenya. In January 2020, the company also raised USD$250,000 from Gray Matters Capital, to finance its planned future expansion to Malawi.

These blockchain platforms can also be used to connect farmers to other farmers, for activities such as asset or land sharing, resulting in more efficiency in economical farming operations. Blockchain platform can also enable small farmers to lease idle farms from their peers, thereby providing them with access to additional revenue sources, which they would not be able to do traditionally.

AgUnity, an Australian-start-up established in 2016, developed a mobile application which enables farmers to record their produce and transactions over a distributed ledger, offering a trusted and transparent platform to work with co-operatives and third-party buyers. The platform also enables farmers to share farming equipment as per a set schedule to improve overall operational and cost efficiency. In Africa, AgUnity has launched pilot projects in Kenya and Ethiopia, targeted at helping farmers achieve better income for their produce.

A Nigerian start-up, Hello Tractor uses IBM’s blockchain technology to help small farmers in Nigeria, which cannot afford tractors on their own, to lease idle tractors from owners and contractors at affordable prices through a mobile application.

Smart contracts to transform agriculture finance and insurance

Less than 3% of small farmers in sub-Saharan Africa have adequate access to agricultural insurance coverage, which leaves them vulnerable to adverse climatic situations such as droughts.

Smart contracts based on blockchain can also be used to provide crop-insurance, which can be triggered given certain set conditions are met, enabling farmers to secure their farms and family livelihood in case of extreme climatic events such as floods or droughts.

SmartCrop, an Android-based mobile platform, provides affordable crop insurance to more than 20,000 small farms in Ghana, Kenya, and Uganda through blockchain-based smart contracts, which are triggered based on intelligent weather predictions.

Netherlands-based ICS, parent company of Agrics East Africa (which provides farm inputs on credit to small farmers in Kenya and Tanzania) is also exploring a blockchain-wallet based saving product, “drought coins”, which can be encashed by farmers depending on the weather conditions and forecasts.

Tracking of assets (such as land registries) and transactions on the blockchain can also be used to verify the farmers’ history, which can be used by alternative financing companies to offer loans or credits to farmers – e.g. in cases when farmers are not able to get such financing from traditional banks – transforming the banking and financial services available to farmers.

Several African start-ups such as Twiga Foods and Cellulant have tried to explore the use of blockchain technology to offer agriculture financing solutions to small farmers in Africa.

In late 2018, Africa’s leading mobile wallet company, Cellulant, launched Agrikore, a blockchain-based digital-payment, contracting, and marketplace system that connects small farmers with large commercial customers. The company started its operations in Nigeria and is exploring expansion of its business to Kenya.

In 2018, Kenya-based Twiga Foods (that connects farmers to urban retailers in an informal market) partnered with IBM to launch a blockchain-based lending platform which offered loans to small retailers in Kenya to purchase food products from suppliers listed on Twiga platform.


Read our previous Perspective Africa’s Fintech Market Striding into New Product Segments to find out more about innovative fintech products for agriculture and other sectors financing in Africa


And last, but not the least, blockchain or cryptocurrencies can simply be used as a mode of payment with a much lower transaction fee offered by traditional banking institutions.

Improving mobile internet access to boost blockchain implementation

While blockchain has shown potential to transform agriculture in Africa, its implementation is limited by the lack of mobile/internet access and technical know-how among small farmers. As of 2018, mobile internet had penetrated only 23% of the total population in Sub-Saharan Africa.

However, the GSM Association predicts mobile internet penetration to improve significantly over the next five years, to ~39% by 2025. Improved access to internet services is expected to boost the farmers’ ability to interact with the blockchain solutions, thereby increasing development and deployment of more blockchain-based solutions for farmers.

EOS Perspective

Agritech offers an immense opportunity in Africa, and blockchain is likely to be an integral part of this opportunity. Blockchain has already started witnessing implementation in systems providing proof of ownership, platforms for farmer cooperation, and agricultural financing tools.

Unlike Asian and Latin American countries, African markets have shown a relatively positive attitude towards adoption of blockchain, a fact that promises positive environment for development of such solutions.

At the moment, most development in blockchain agritech space is concentrated in Kenya, Nigeria, Uganda, and Ghana. However, there is potential to scale up operations in other countries across Africa as well, and some start-ups have already proved this (e.g. Farmshine was able to secure the necessary financing to expand its presence in Malawi). Other companies can follow suit, however, that would only be possible with the help of further private sector investments.

Still in the nascent stages of development, blockchain solutions face an uncertain future, at least in the short term, and are dependent on external influences to pick up growth they need to impact the agriculture sector significantly. However, once such solutions achieve certain scalability, and become increasingly integrated with other technologies, such as Internet of Things and artificial intelligence, blockchain has the capability of completely transform the way farming is done in Africa.

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