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Bridging the Gap between MDx Testing and Point-of-care by EOS Intelligence
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Bridging the Gap between MDx Testing and Point-of-care

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The COVID-19 pandemic brought innovation and investment to the in vitro diagnostics (IVD) market, opening new pathways to simplify and expand testing. The previously complicated and time-consuming molecular testing gradually started moving towards rapid testing, changing how we manage healthcare. The growing popularity of rapid testing gave way to self-sampling and at-home sampling, which is set to bring molecular testing closer to patients. Another noticeable transformation the industry witnessed post-pandemic was the rise of molecular testing at point-of-care (POC), which is set to disrupt the way clinicians deliver accurate diagnoses in record time.

The latest generation of IVD devices is focused on providing quick diagnosis and being cost-effective. This has led to IVD companies focusing on developing simpler and less invasive sample collection methods, such as self-sampling tests.

IVD innovation is also transforming molecular testing to make healthcare more accessible. To a certain extent, dependence on laboratories is gradually decreasing with molecular testing available at POC. A key development in this area is the use of multiplex assay, which allows to test for multiple pathogens simultaneously, allowing for early diagnosis.

Molecular testing moving near-patient

After using antigen tests during COVID-19, demand for molecular testing for a variety of diseases at POC has risen drastically. In 2023, the industry faced an acute shortage of skilled laboratory staff, further increasing the need for molecular testing to move near-patient. This has resulted in physicians and patients preferring molecular tests at POC (MPOC). Some prominent industry players, such as Cepheid, Abbott, and BioFire, offer CLIA-waived PCR instruments and multiplex assay tests for the POC setting. A CLIA-waived certification allows tests to be performed at a doctor’s office by a non-technician instead of other more complex MDx tests requiring specialized technicians.

Moving these multiplex molecular tests near-patient is revamping the IVD landscape, positively impacting both the patients and payers. Early diagnosis with POC diagnostics empowers physicians with evidence-based decision-making at an early stage. Moreover, with multiplex assays increasingly being used for MPOC and delivering results within 10-25 minutes (in the case of respiratory assays), the wait time for patients to receive the correct diagnosis has reduced substantially. This results in clinicians being able to start with proper treatment on the patient’s first visit, thus reducing the total number of patient visits. Consequently, physicians are also able to accommodate a higher number of patients.

In fact, MPOC could become a critical element of the value-based care model in the USA. The value-based program incentivizes healthcare providers/physicians to provide quality healthcare. With MPOC offering quicker turnaround time and lower testing costs, physicians/payers will likely be better incentivized and motivated to deliver high-quality services.

Growing demand for self-sampling/at-home sampling

The pandemic raised public awareness regarding the use of self-sampling kits and increased demand for them. Further, the FDA granted Emergency Use Authorization to multiple assays during the pandemic to quickly onboard self-test kits and penetrate the US households with this novel testing method.

Driven by the convenience, cost-effectiveness, and accessibility offered by self-sampling kits, they are becoming increasingly popular, particularly amongst the aging population that needs tools and technologies to manage health at home. It is also proving to be a sustainable testing method, as it can be used for preventative screening as well as allows for discretion for patients who may not prefer to get tested in a laboratory or by a physician, particularly in case of sexually transmitted infections (STIs).

Additionally, unlike OTC tests, molecular diagnostic tests allow for better accuracy in results and are recognized by the FDA for clinical diagnosis use. This has given confidence to healthcare providers to advocate self-sampling, as they stand to benefit from bringing care to patients’ homes, eventually reducing healthcare expenses. In a value-based setting, at-home testing proves to particularly benefit physicians who are able to eliminate unnecessary patient visits.

For the prominent industry players, at-home testing represents a key opportunity area to grow in the niche direct-to-consumer testing segment. Companies are also using these tests as an opportunity to target the rural population who do not have easy access to laboratories. Besides infectious and respiratory diseases, companies are now trying to foray into other treatment areas, such as human papillomavirus (HPV). Self-sample collection for HPV has begun in Europe with BD’s Onclarity HPV assay.

EOS Perspective

Establishing a strong foothold in both self-sampling and MPOC segments is seen as a sizeable business opportunity for stakeholders of the IVD market. In the near term, it is likely for the IVD players to continue launching new assays and technologies to expand offerings.

For self-sampling, MDx players have been focusing on infectious diseases, and there still is a vast untapped market for self-sampling at home, specifically when testing for STIs. In November 2023, LetsGetChecked became the first company to secure FDA approval for chlamydia and gonorrhea at-home sample collection. This has opened doors for other players to enter this niche market, and they are likely to jump on the bandwagon by seeking FDA approvals for their STIs self-sampling kits. Major players, such as Hologic, are already gathering data to launch a self-collection device for STIs. Hologic’s Aptima Swab for STIs multi-testing is approved in the EU, and the company is now conducting trials to get approval in the USA.

In the near term, a noticeable trend in the MPOC segment is expected to be the focus of MDx players on developing multiplex assays that follow the ‘one-size-fits-all’ approach. There is a growing demand from physicians for multiplex assays that allow them to test for multiple viruses and deliver results in under four hours. Companies have already started to take matters into their own hands by focusing their R&D efforts on developing panels and preparing them for FDA approval and CLIA waiver. Becton Dickinson announced the launch of its first molecular diagnostics POC instrument, BD Elience, by 2025. The device is expected to allow panel testing for respiratory and sexually transmitted diseases.

Although the self-sampling and MPOC segments present many opportunities for the IVD stakeholders, some roadblocks may hinder their development and adoption. For instance, multiplex assay reimbursement schemes may hamper their widespread adoption in the POC setting. Per the latest guidelines, reimbursement schemes for multiplex assays are less favorable than those for singleplex assays. Furthermore, at present, there are no reimbursement schemes in place to reimburse for self-sampling at home, so patients are required to pay out-of-pocket.

Several players face a crucial challenge for at-home collection: proving to the FDA that the self-sample collected is not contaminated or poorly taken. FDA requirements for approval of these tests are very stringent and demand that companies prove the adequacy of the sample collected by patients to match that of laboratory collection.

Despite these challenges, self-sampling and MPOC present untapped opportunities for many IVD players seeking to expand their capabilities and offerings to position themselves better in the MDx market.

Genetic Testing Fraud – The Next Big Concern for the US Healthcare Industry by EOS Intelligence
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Genetic Testing Fraud – The Next Big Concern for the US Healthcare?

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Over the past few years, lab fraud has become a concern in the USA with the increase in financial gains obtainable through fraudulent billing practices, unnecessary testing, bundling of expensive tests (such as tests for rare respiratory pathogens or genetic tests) with COVID-19 tests, and increase in the number of genetic testing labs. A recent update in the compliance and regulatory requirements and increased focus on analyzing fraud testing schemes are expected to help curb lab fraud in the country.

Genetic testing, due to its increased use in the healthcare industry, is a particularly lucrative fraud target. Despite the presence of various compliance programs and regulations, several laboratories, together with patient brokers, telemedicine companies, and call centers, commit fraud and defraud Medicare. This strains the healthcare system as it increases healthcare costs and influences the patients’ trust in testing, labs, and other stakeholders.

Clinical labs face less scrutiny than full-service health centers. Thus, they are more frequently involved in lab fraud activities. Some of the most commonly noticed lab fraud cases in the USA include kickback schemes, fraudulent billing, and unnecessary testing, among others. Labs team up with parties such as patient brokers to get patients, doctors to refer patients or prescribe unnecessary tests, telemedicine companies to order tests, and call centers to target Medicare beneficiaries and then defraud Medicare by submitting claims.

Lab fraud in genetic testing has emerged in the USA over the past few years due to sprouting genetic testing labs across the country and the increasing use of such tests in health practices to assist disease diagnosis and predict disease risk. Genetic testing enables healthcare providers to offer personalized medicine based on the individual’s genetic makeup and helps identify how the patient will respond to treatments. Genetic testing fraud, mainly targeting cancer screening, pharmacogenetics, and cardiovascular diseases, is on the rise.

One of many such fraud cases was noted in August 2023, when LabSolutions LLC, based in Georgia, USA, submitted over US$463 million worth of unnecessary genetic and other laboratory tests to Medicare, the national health insurance program, of which Medicare paid over US$187 million. These tests were obtained through kickbacks and bribes. The scale of similar fraud is evident from the fact that in July 2022, the Department of Justice announced criminal charges against 36 defendants in 13 federal districts for more than US$1.2 billion in fraudulent telemedicine, cardiovascular and genetic testing, and durable medical equipment purchases.

The COVID-19 outbreak in 2020 further spiked fraud cases, as it gave an opportunity to bundle COVID-19 testing with other forms of expensive testing that patients did not need, including genetic testing for various diseases and tests for rare respiratory pathogens. Financial incentives offered by the federal government to encourage participation in COVID-19 control-related businesses also attracted fraudsters in the laboratory business. According to the US Department of Health and Human Services report, in May 2023, around 378 labs billed Medicare Part B for add-on COVID-19 tests at high volume and payment amounts. Of these, around 276 labs billed for more add-on tests, such as billing Medicaid for COVID-19 tests alongside respiratory pathogen panels (RPPs), individual respiratory tests (IRTs), allergy tests, and genetic testing. An additional 161 of these 378 labs also reported higher costs than usual for add-on testing.

Lab fraud behind money loss, erosion of trust, and increased insurance premiums

Lab fraud causes a significant adverse effect on the integrity and quality of the healthcare system as unnecessary testing and fraudulent billing practices increase healthcare costs, compromise the accuracy and reliability of diagnostic tests, and erode trust in healthcare providers, including doctors and hospitals, among others. Healthcare providers who unknowingly refer patients to fraudulent labs are also likely to face a reputation hit.

Above all, healthcare fraud can cause tens of billions of dollars in yearly losses. According to the National Health Care Anti-Fraud Association, taxpayers are losing over US$100 billion annually to Medicare and Medicaid fraud, including billing for unapproved COVID-19 tests, genetic testing fraud, home healthcare billing, and fraud billing for medical equipment.

Companies manufacturing genetic testing kits may face reputational damage if their products are used in the genetic testing fraud scheme. This is expected to negatively impact their market presence as customers/patients will lose confidence and will likely move to reputed competitors. Also, healthcare providers may stop referring the company products to their patients.

Increasing fraud will likely drive the need for more stringent regulations for genetic companies manufacturing genetic testing kits (requiring compliance in conducting in-depth clinical studies, providing extensive data, maintaining necessary documentation, labeling and packaging requirements, etc.). This is expected to increase the operational costs for genetic testing companies and, thus, the price of genetic testing services. Ever-increasing genetic testing fraud is expected to potentially disrupt the market’s growth trajectory as patients become more cautious. Individuals are likely to receive tests that are not appropriate or required and may become skeptical about the necessity and accuracy of the test result.

Read our related Perspective:
Commentary: The Promise of Comprehensive Genomic Profiling in the USA

Lab fraud also increases insurance premiums as fraudulent activities increase the cost of claims, which in turn increases insurance companies’ expenses. The insurance companies are bound to raise premiums to cover additional costs. Additionally, individuals receiving genetic testing through fraud schemes will likely be denied future coverage. This is because many genetic tests for inherited diseases are offered as a one-time payment for a lifetime of coverage, and fraud schemes can compromise the individual’s access to this benefit.

Regulatory updates and strategies aimed at combating lab fraud

Preventing lab fraud is crucial to maintaining the integrity of scientific research and the functioning of healthcare systems. Lab fraud can be prevented, or at least significantly diminished, by establishing comprehensive compliance programs, stringent licensing and certification requirements for labs and healthcare providers, encouraging employees and stakeholders in labs and healthcare organizations to report any suspected fraud incidences, education, secured data handling, continuous monitoring, improved medical billing processes, and enforcing penalties and legal consequences.

In January 2023, the US government updated compliance and regulatory requirements for laboratories to prevent lab fraud. As per the updates, the laboratories must submit a medical necessity document supporting the ordered test, progress note, and the treating doctor’s signature to support a claim.

Also, providing incentives to physicians to encourage them to refer patients for lab services will be considered a violation of the federal Anti-Kickback Statute, and both laboratory and healthcare professionals will face legal consequences.

Laboratories that fail to adhere to lab billing guidelines published through National Coverage Determinations (NCDs) or Local Coverage Determinations (LCDs) will face civil liability and triple damages under the False Claims Act.

The government also continued its scrutiny of medically unnecessary genetic testing schemes, audited genetic labs, and tried to recoup funds where the medical necessity requirement was unmet. Also, the Office of Inspector General (OIG) issued a fraud alert warning the public about the proliferation of COVID-19 testing and genetic testing scams.

Moreover, in June 2023, the US Food and Drug Administration (FDA) took a crucial measure to regulate an extensive array of laboratory tests, including prenatal genetic screenings, to ensure test result accuracy and prevent unreliable outcomes. The US FDA ensures that the lab test delivers results as claimed by the lab test developer by analyzing the device’s accuracy, specificity, clinical characteristics, and analytical sensitivity. Regulating these laboratory tests will likely reduce the chances of fraud, as laboratories will not be allowed to run specific tests if they are not cleared or approved by the FDA.

EOS Perspective

Increased awareness about genetic testing and its easy accessibility have made it more vulnerable to lab fraud in the country. Genetic testing scams are evolving significantly wherein the scammers (a lab owner or a genetic testing company’s representative) are offering free screening, cheek swabs, or testing kits for genetic testing to get the individual’s Medicare information and submit claims. An increase in the number of genetic testing companies manufacturing direct-to-consumer genetic testing kits is expected to further contribute to genetic testing fraud as it will become easier for lab owners to get access to genetic testing kits and scam Medicare beneficiaries.

Also, the introduction of new tests creates potential opportunities for lab fraud as the lack of proper oversight and safeguards makes it easier for lab fraudsters to exploit gaps while appropriate regulatory norms for those tests are being developed. Thus, there is an increased need to set the regulatory norms for any new tests being developed before they are put to use.

While various compliance and regulatory measures are in place to prevent lab fraud, ethical practices, education, and training for lab employees will likely play a significant role in preventing lab fraud in the country. Many healthcare professionals are often involved between doctors prescribing the test and the persons administering the test. Thus, it becomes challenging to determine whether the referrals are conducted efficiently.

In addition, strong collaboration among healthcare insurers, healthcare providers, and the government can also help prevent this kind of fraud. The government plays a vital role here, as it has the tools to lay more emphasis on continuous monitoring and auditing of genetic testing labs to keep track of lab activities and prevent fraud cases.

New Directions in Alzheimer’s Diagnostics Will Blood Tests Replace CSF and PET by EOS Intelligence
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New Directions in Alzheimer’s Diagnostics: Will Blood Tests Replace CSF and PET?

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Around three-fourths of dementia cases continue to remain undiagnosed even though the incidence of Alzheimer’s disease (AD) is rapidly growing across the globe. AD affects about 60-80% of dementia patients worldwide. Early diagnosis of AD is critical in forging beneficial medical care strategies and enhancing patient outcomes. Current AD diagnostic tests, such as cerebrospinal fluid (CSF) and PET scans, are either invasive or associated with side effects and are generally expensive. This calls for developing less invasive, safer, faster, and more accurate AD diagnostics, such as blood tests.

Blood-based tests promise accurate and non-invasive AD diagnosis

Researchers are developing less invasive and less costly blood tests that are likely to be more accurate than contemporary tests. There are currently two types of AD diagnostics blood-based tests: the phosphorylated tau217 (ptau217) test and the amyloid beta (Aβ) 42/40 plasma ratio test.

The ptau217 biomarker has the potential to differentiate AD from other neurodegenerative diseases, as ptau217 levels can be high in AD patients before the onset of clinical symptoms. Studies have proved that ptau217 tests can detect AD early on and monitor disease progression.

The Aβ 42/40 plasma ratio tests detect amyloid beta protein plaques in the brain that cause cognitive impairment. Due to the lack of a certified reference standard for measuring plasma Aβ42 and Aβ40’s absolute values, ptau217 may be better than an amyloid beta ratio test. However, both tests are accurate enough to diagnose AD.

Notably, ptau217 blood tests are believed to give up to 95% accurate results when coupled with CSF tests as against 90% accuracy of CSF when used as a standalone method. At the same time, amyloid beta (Aβ) 42/40 ratio tests are known to give around 80% accuracy in detecting amyloid positivity.

Many laboratories and diagnostic companies have designed or are designing ptau217 assays. C2N Diagnostics, Quanterix, Quest Diagnostics, and Laboratory Corporation of America (LabCorp) offer ptau217 laboratory-developed tests (LDTs).

Low cost of blood-based AD tests can also be a growth-driving factor

A major push towards blood-based AD diagnostics comes from the tests’ lower cost in comparison to PET and CSF. The cost of blood tests typically ranges from US$200 to US$1,500, depending on the test provider.

The cost of PET ranges from US$1,200 to US$18,000, while the average price of CSF tests is around US$4,000 (in both cases, the actual cost depends on the type of facility, location, and the extent of insurance coverage).

As of 2023, Medicare and Medicaid covered PET scans for AD in the USA outside clinical trials. Therefore, AD patients need to pay around 20% of the PET cost, which translates to US$240-US$3,600, even after insurance coverage.

Considering the high share of dementia and AD cases remaining undiagnosed, there is a chance that the lower cost of blood-based tests can help contribute to higher accessibility to testing and ultimately improve the early detection rate.

Large AD diagnostic players partner with smaller ones to develop new tests

In an attempt to develop ptau217 assays, major diagnostics companies tend to recognize the development progress made by smaller players. ALZpath, a novel AD diagnostic solutions provider, is the pioneer of the ptau217 antibody, which helps in the early detection of the disease. Large players such as Roche and Beckman Coulter are enticed by the synergistic opportunities ALZpath offers.

In June 2024, Roche partnered with ALZpath, an early-stage biopharmaceutical company specializing in AD diagnostics, to launch the plasma ptau217 In-Vitro Diagnostic (IVD) test. As per the partnership, Roche will use ALZpath’s ptau217 antibody to design and commercialize an IVD test to detect AD with the help of Roche’s Elecsys platform.

In July 2024, Beckman Coulter also partnered with ALZpath to utilize ALZpath’s proprietary ptau217 antibody to detect AD on Beckman Coulter’s DxI 9000 Immunoassay Analyzer.

AD diagnostics firms receive funding from various sources, including drugmakers

Constantiam Biosciences, a bioinformatic analysis firm, received a US$485,000 Phase 1 SBIR grant (Small Business Innovation Research) from the National Institute on Aging to develop a tool for deciphering risk variants pertaining to AD and related dementias (AD/ADRD) in September 2024.

Biogen and Eli Lilly invested in the Diagnostics Accelerator, a funding initiative started in 2018, at the Alzheimer’s Drug Discovery Foundation (ADDF) in 2020. The Diagnostics Accelerator has invested over US$60 million across 58 projects, most of which are blood tests. In its Q4 2023 earnings call, Biogen emphasized its support for developing tau biomarker diagnostics and pathways. Its partner, Eisai, has invested around US$15 million in C2N Diagnostics and collaborated with IVD companies such as Sysmex, among others. In September 2024, ADDF invested US$7 million in C2N Diagnostics to further develop blood-based AD detection tests.

Other investors have also identified the opportunities AD diagnostic offers. A 2024 market research report by Market Research Future estimated that the AD diagnostic industry would nearly double, from US$4.5 billion in 2023 to US$8.8 billion in 2032.

FDA stands as an accelerating force for blood-based tests via breakthrough device designation

For a while now, the FDA has been granting breakthrough device designation (BDD) to devices that could address life-threatening diseases with unmet medical needs. BDD facilitates the expedited development, review, and assessment of medical devices, ensuring quicker access for patients and medical professionals. It would not be too ambitious to conclude that strong positive evidence from several uses and studies of ptau217 tests is likely to compel the FDA to approve them for use in the near future. The first sign of this is that the FDA is granting BDD status to multiple ptau217 blood tests.

In March 2024, the FDA granted BDD to Simoa ptau217 by Quanterix. This blood test can detect AD in patients with cognitive ailments even before signs and symptoms start to appear.

In April 2024, the FDA gave BDD to Roche’s Elecsys ptau217 plasma biomarker test to augment early diagnosis of AD. Roche partnered with Eli Lilly to develop this blood test that will widen and accelerate AD patients’ access to diagnosis and suitable medical attention and care.

In early 2019, the FDA gave BDD to C2N Diagnostics’ blood test to detect AD. The BDD status of AD blood tests will likely accelerate the development, review, and assessment processes of these tests, improving patient outcomes.

Some FDA-approved AD drugs have used blood tests in clinical trials. Eli Lilly’s Kisunla and Esai/Biogen’s Leqembi have successfully utilized C₂N Diagnostics’ Precivity-ptau217 blood biomarker in their clinical trials. The FDA approved both drugs to manage AD. This improves the chances of this blood test getting approved by the FDA.

Lumipulse G β-Amyloid 1-42 Plasma Ratio test by Fujirebio Diagnostics received BDD from the FDA in 2019. The company submitted an FDA filing for the Lumipulse G ptau217/β-Amyloid 1-42 Plasma Ratio IVD test in September 2024. If approved, this test will become the first commercially available blood-based IVD test in the USA to detect AD.

EOS Perspective

There has been considerable progress in developing blood-based assays for AD diagnosis by pharma and diagnostics companies. However, a good portion of the liability for their products not reaching market readiness faster lies (and will probably remain to lie) on the approving authorities that are unable to accelerate the administrative steps.

Some blood tests, such as PrecivityAD, are approved for safe use in the EU but are still not in the USA. While such approval is typically a time-consuming process and requires a thorough investigation, the blood tests will enter the market at a larger scale across several geographies only if the authorities fast-track their approvals. This is particularly applicable to blood tests previously successfully used in clinical trials for approved AD drugs and for tests that have already attained BDD status from the FDA.

As an example, PrecivityAD by C2N Diagnostics received BDD status in 2019 from the FDA. However, the FDA has still not approved the blood test for safe use in the USA. This is still despite the fact that PrecivityAD and other C2N Diagnostics’ assays have been utilized in over 150 AD and other research studies across the USA and abroad. FDA’s time-consuming and lengthy review procedures and bureaucratic reasons are some of the factors responsible for the delay in approval. In addition to this, C2N Diagnostics needs to submit some more evidential data pertaining to the accuracy of PrecivityAD, which is likely to take time to produce.

These procedural and administrative impediments, along with the time taken by the device makers to present the data to the FDA, will likely continue to put a brake on the blood-based tests becoming available to patients in the near future.

The situation will remain so, given the FDA’s recent decision to regulate new LDTs involving diagnostic tests that use body fluids such as blood, saliva, CSF, or tissue on similar lines as medical devices (meaning LDTs must comply with the same standards as medical devices). As per this regulation, LDTs need to prove the accuracy of their tests. This decision will have both winners and losers in the AD stakeholder ecosystem.

Researchers and physicians are looking at this regulation with a positive stride as this step will reduce the number of tests with unconfirmed accuracy from the market in the USA. This is undoubtedly a positive change for patients’ safety, reducing the number of misdiagnoses and accelerating correct diagnoses.

On the other hand, smaller start-ups and diagnostic companies are not likely to benefit from this decision as it will restrict the development of new innovative tests vis-à-vis large diagnostic companies. Overall, the decision will likely decelerate the approval of blood-based AD tests or at least will require much more paperwork and proof of accuracy from the device makers. This decision will take effect in multiple phases over four years, starting from July 2024.

On the research and development side of the Alzheimer’s disease diagnostics space, a certain level of symbiosis between drug producers and diagnostic solution providers will continue to impact the market positively. Drugmakers are partnering with or investing in diagnostic companies to leverage the latter’s innovative blood-based biomarkers (BBBM) technologies in the clinical trials of their own drug candidates. This trend is likely to continue.

Not only drugmakers but also more prominent healthcare diagnostics companies, such as Roche and Beckman Coulter, are partnering with early-stage biopharmaceutical companies, such as ALZpath, to develop and commercialize AD ptau217 tests. Collaborations such as these are a testimony to the fact that it is mutually beneficial for AD industry stakeholders to work in tandem to advance AD diagnostics research, a significant growth-driving factor for the market.

Powering Healthcare Diagnostics with AI by EOS Intelligence
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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.

Recall Aftermath Who is Gaining Share in the Sleep Apnea Devices by EOS Intelligence
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Recall Aftermath: Who is Gaining Share in the Sleep Apnea Devices and Ventilators Market?

In recent years, the number of ventilator recalls has increased considerably, primarily due to product quality issues, software malfunction, and manufacturing defects. This affected manufacturers such as Philips, Medtronic, and Vyaire Medical, leading to brand damage, financial losses, and a shift in the market competition. Existing players and new entrants such as Getinge and Nihon Kohden are stepping in to fill the gap with innovative and non-invasive products. The recalls caused challenges for manufacturers and patients, highlighting the need for strong quality control and regulatory oversight.

Recalls of its sleep apnea devices and ventilators hit Philips the hardest

The medical device industry has recently experienced many product recalls, particularly in the ventilators segment, impacting major market players such as Philips, Medtronic, Baxter, GE Healthcare, Hamilton Medical, and Vyaire Medical.

Philips (Philips Respironics) faced a series of class I respiratory product recalls, including CPAP and BiPAP machines, and ventilators, due to health risks caused by the polyester-based polyurethane (PE-PUR) sound abatement foam breakdown in the devices. Industry experts consider Philips’ sleep apnea devices and ventilator recalls among the most significant since 2021. As of January 2024, the company experienced a recall of over 15 million sleep apnea devices and ventilators, and reportedly hundreds of deaths. The recall seriously hurt the company’s reputation, weakened its position in the market, and caused significant financial problems.

The recalls led to a decline in the company’s share price by 60-70% in 2021, and it is still about 50% lower than its peak in April 2021 (US$ 53.45). Comparable sales of the connected care segment, including sleep apnea devices and ventilators, declined by about 19% in 2021 in comparison to 2020. This happened primarily due to sleep apnea devices and ventilators recalls, and the normalization of demand for hospital ventilators and monitoring systems following the COVID-19 surge. Recalls continued to drive down ventilator and sleep apnea device sales in 2022 and 2023.

The considerable impact on sleep apnea devices and ventilator sales resulted in a decline in Philip’s share in the sleep apnea device market, dropping to an estimated 20% between 2021-2023 from over 30% before the recall. The company also experienced a notable decline in market share in the ventilators market. Despite the decline in market share, Philips maintained its position as one of the leading players in both the sleep apnea devices and ventilators market.

However, in January 2024, Philips agreed to halt the sales of 19 sleep and respiratory products in the USA as a part of the consent decree with the US Department of Justice (DOJ). These products included hospital ventilation, certain home ventilation, sleep diagnostic devices, and portable and stationary oxygen concentrators. This affected the company’s brand image greatly and resulted in a further loss of market share in both ventilators and sleep apnea devices markets. Since the company will continue to sell consumables and accessories, including masks, it is anticipated to maintain a portion of its market share in both segments.

In April 2024, the company agreed to pay US$1.1 billion in legal settlement to resolve injury-related cases caused by sleep apnea devices and ventilators in the USA. Overall, sleep apnea device recalls cost the company over US$5 billion, likely including charges such as provisions for Philips Respironics-related litigation, consent decree, remediation costs, legal settlements, workforce restructuring, and quality remediation action. In addition, Philips cut 6,600 jobs by 2023 and is likely to reduce its workforce by a total of 10,000 by 2025.

Several companies bore the brunt of their own ventilator recall setbacks

Other prominent manufacturers such as Drägerwerk (Draeger), Medtronic, Vyaire Medical, Hamilton Medical, and Baxter also experienced various ventilator recalls due to manufacturing and quality defects. Although the FDA classified these recalls as serious, these companies did not face the same severe consequences as Philips, as these recalls did not result in major injuries.

All these manufacturers also witnessed a drop in ventilator sales largely due to the stabilization of demand for ventilators following the COVID-19 surge, with product recalls also contributing to the downturn.

In February 2024, Medtronic completely exited the ventilator market due to unprofitability. Similarly, in June 2024, Vyaire Medical filed for bankruptcy and was subsequently acquired in October 2024 by Zoll, an Asahi Kasei company engaged in the manufacturing of medical devices and related software solutions. This caused a profound impact on the ventilators market.

Market players are introducing products with advanced features to gain market share

The ventilator market encountered a radical shift in competition due to numerous product recalls. The suspension of sleep and respiratory product sales cost Philips its leading market position in sleep apnea devices and ventilators (except for certain home ventilators). It remains unclear when or if Philips will be able to resume sales of these devices. However, the company is unlikely to leave its presence in the sleep apnea devices and ventilators market entirely due to its commitment to service and supply of parts of ventilators in use, as well as its decision to continue the sale of consumables and accessories.

Existing market players such as Getinge, Hamilton Medical, Drägerwerk (Draeger), ResMed, and GE Healthcare, and newer entrants such as Nihon Kohden, are likely to fill in the gap left by Philips, Medtronic, and Vyaire Medical in the USA.

Market players such as Getinge, Drägerwerk (Draeger), and Nihon Kohden are focusing on introducing technologically advanced ventilators with features such as enhanced patient comfort, advanced monitoring capabilities, portability, and adaptive ventilation modes, to grab a slice of the pie. They are also increasingly focusing on expanding their portfolio of non-invasive ventilators with different interfaces, including face masks, nasal masks, helmets, and mouthpieces.

For instance, in October 2024, Nihon Kohden introduced a new ventilator system that combines invasive and non-invasive ventilation and high-flow oxygen therapy in one device, offering adaptability and eliminating the need to switch between machines. It also features a customizable, app-based touchscreen interface with advanced monitoring capabilities. Similarly, in January 2024, Getinge introduced ‘Servo-air Lite’, a non-invasive ventilator with high-flow therapy that offers optimal respiratory support, enhanced patient comfort, and ease of use for clinicians.

ResMed, a leading player in both the sleep apnea devices and ventilators market, is estimated to have grabbed over 10% of Philips’ market share in the sleep apnea devices market in the USA. ResMed witnessed a substantial increase in demand for its sleep and respiratory care products, including sleep apnea devices and ventilators, for various reasons, including Philips’ product recalls. The demand for its sleep and respiratory care products in the USA, Canada, and Latin America increased by 16%, 25%, and 10% in 2022, 2023, and 2024, respectively.

Companies engaging in sleep apnea devices and ventilator rentals, sales, and distribution, such as Trace Medical, also started adding brands from different companies to their product mix to meet the demand for these devices.

Patients experience delays in treatment and struggle to switch to other brands

Philips’ foam degradation issue has exposed patients to severe health risks, leading to respiratory complications and even cancer. Recalls of many ventilators and sleep apnea devices have left hospitals struggling to replace them, causing delays in patient treatment.

Patients relying on a specific brand faced reduced treatment options. Many patients found it difficult to switch to other brands due to cost and differences in machine settings or interfaces. With Philips halting sales of various sleep apnea devices and ventilators, patients have no choice but to switch to other brands.

The recall of various products from different companies has created significant demand and supply chain pressures for existing companies. These pressures will likely drive up ventilator and sleep apnea device prices, further burdening patients.

EOS Perspective

Product recalls in the sleep apnea devices and ventilator segment brought quality issues to the limelight. This highlights the need for stronger quality control processes and technologically advanced sleep apnea devices and ventilators incorporating virtual monitoring and AI integration, which can help detect defects earlier.

While the FDA received complaints about Philips’ degradation of the sound abatement foam in the sleep apnea devices and ventilators before the recall initiation, decisive action to force correction was not taken immediately. Also, despite knowing that Philips had been aware of the foam degradation issue for many years, the FDA did not take stronger enforcement measures against the company sooner. This situation highlights the importance of assessing and enhancing the FDA’s oversight process to ensure timely response to medical device complaints.

Philips suffered lasting brand damage due to the recalls. Although the company is trying to regain shareholder and consumer trust after settling US claims for an amount much lower than anticipated (US$2-5 billion) by analysts and the public, it faces a long road ahead.

Regarding market competition, ResMed is estimated to continue to lead and strengthen its dominant position in the sleep apnea devices market. The exit of well-established players from the ventilator market will intensify competition among existing companies and new entrants seeking to capture market share. However, it will be a gradual process as customers slowly transition from existing products to new brand ones. On top of that, the new entrants are likely to face stricter regulatory norms and product approval processes aimed at reducing the number of product recalls and enhancing patients’ safety.

DeepSeek’s Disruption Reshaping the Global AI Battlefield by EOS Intelligence
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DeepSeek’s Disruption: Reshaping the Global AI Battlefield

In January 2025, DeepSeek AI introduced two powerful large language models (LLMs) that shook the AI world. Developed at a fraction of the cost of its existing peers, DeepSeek holds the potential to transform the AI development landscape globally.

DeepSeek’s efficiency enables better cost-effectiveness by reducing computational needs

DeepSeek’s V3 and R1 models focus on efficiency and require less computing power than rival models while delivering equivalent performance. Its efficiency stems from using the “Mixture-of-Experts” (MoE) architecture, which activates only parts of the model for a given task, minimizing computational needs. This targeted use of computational memory reduces operational costs, giving it a significant edge over competitors who rely on more resource-intensive approaches.

The arrival of DeepSeek has sent shockwaves throughout the US tech industry, marked by a significant decline in stock values. The key headline event was Nvidia’s US$600 billion drop in market capitalization. The fact that a Chinese company was able to achieve groundbreaking results at a fraction of the cost by using low-power Nvidia H800 chips challenges the investment poured into the market by established players.

The open-source model enables widespread applications at a budget

DeepSeek has embraced a fully open-source model, allowing anyone to utilize their technology for commercial purposes. DeepSeek’s open-source approach democratizes access to AI, enabling a wider range of applications.

The availability of DeepSeek’s advanced APIs at a very low cost also appeals to customers who have previously been priced out of advanced AI applications due to the higher costs of proprietary LLM models such as OpenAI’s GPT.

The AI ecosystem already feels the impact of DeepSeek’s triumph. Its free AI assistant has also made a significant splash in the consumer market, with DeepSeek’s app surpassing ChatGPT in Apple Store charts. Its cost-effectiveness has even attracted the attention of major players such as AWS and Snowflake, which are now offering DeepSeek’s technology on their platforms.

Following DeepSeek’s success, several other Chinese companies may follow suit by developing more efficient yet high-performing AI models, further driving the costs down. Alibaba already released a new version of its Qwen 2.5 model at the end of January 2025.

Initial success of DeepSeek does not guarantee dominance

DeepSeek’s success does not guarantee its dominance in the AI landscape. We have had precedence of a similar company making headway and then falling off in the AI space. Mistral’s open-source AI model, Mixtral 8x7b, initially seemed poised to disrupt the field. However, it quickly fell off the radar when other closed-source models incorporated Mixtral’s innovations.

DeepSeek’s continued success will depend on whether it is able to maintain its edge through continuous innovation, particularly with limited access to high-performance chips.

Read our related Perspective:
 NVIDIA’s Meteoric Rise: Can the AI Chip Giant Sustain Its Dominance?

EOS Perspective

DeepSeek’s emergence as a serious contender has intensified the global AI race, challenging the dominance of established players such as OpenAI, Meta, and Google.

DeepSeek-R1, with its open-source foundation, has already demonstrated impressive abilities in handling complex text-based tasks, such as summarizing documents, answering technical questions, and generating codes. Moreover, it offers these capabilities through APIs at a fraction of the cost of its competitors, potentially disrupting the market and driving down prices for AI services.

With DeepSeek’s AI models requiring less computational power and hardware, they will offer significant cost savings for users. Combined with its open-source model, which fosters customization, collaboration, and broader access, DeepSeek is expected to gain traction rapidly. While it is currently limited to text-based queries, its potential is undeniable.

While questions about Chinese government influence and censorship persist, DeepSeek presents a compelling vision of AI disruption. In the short term, we can anticipate lower AI adoption costs and shrinking profit margins for established AI providers. Furthermore, DeepSeek’s emphasis on efficiency could spark a shift in the industry, prioritizing resource optimization over simply increasing computing power. The full scope of DeepSeek’s impact, however, will only unfold over time.

Open Banking Sparking a Wave of Innovation in Financial Services by EOS Intelligence
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Open Banking Sparking a Wave of Innovation in Financial Services

The adoption of open banking is leading to innovation across financial solutions such as account-to-account payments (A2A), personal finance management (PFM) apps, embedded finance, and banking-as-a-service (BaaS) by enabling real-time data-driven insights and personalized financial services. It is paving the way for a more dynamic financial landscape. Open banking has evolved rapidly since the revised Payment Services Directive (PSD2) came into force in Europe. While challenges exist, adopting open banking solutions, aided by introducing regulatory and security measures, holds the potential to revolutionize the financial services sector.

The introduction of APIs transformed banking services

Open banking has emerged as a transformative force, changing how financial data is shared, and services are offered to consumers. It securely provides third-party financial service providers access to consumer’s financial information with their consent through an application programming interface (API). It aims to foster innovation in financial services, encourage healthy competition, and give consumers more control over their banking information. Several banks across countries, including Citi, Barclays, and Deutsche Bank, have started providing access to their APIs.

Regulatory initiatives and consumer demand lead to open banking growth

While open banking has existed for a long time, it gained traction when the PSD2, a European regulation focused on creating a more open, competitive, and secure payment landscape across Europe, came into effect in 2018.

Since then, several countries have introduced open banking regulations to support its adoption. For instance, in the UK, the open banking initiative, led by the Competition and Markets Authority (CMA, the UK’s principal authority responsible for strengthening business competition and preventing anti-competitive activities), became effective in 2018. In addition to the European countries, Australia, New Zealand, Brazil, and South Africa, among others, have introduced regulatory measures to drive the adoption of open banking.

Countries across the globe are adopting various approaches to open banking, including regulatory-led, market-led, and hybrid approaches. While Europe has taken a regulatory-led approach, adopting open banking in the USA, Canada, and China is driven by consumer demand and technological innovations. Consumers prefer to have control and transparency over their financial data. While there are currently no regulatory frameworks for open banking in the USA, the Consumer Financial Protection Bureau (CFPB) has proposed rules to protect consumer data rights, which will aid in facilitating the adoption of open banking.

Several countries, such as India, South Korea, Japan, Hong Kong, Russia, and Singapore, have adopted a hybrid model, including both regulatory and market-led initiatives. These countries do not have mandatory open banking regimes, but policymakers are looking to introduce initiatives to accelerate open banking adoption. For instance, in Singapore, the Monetary Authority of Singapore (MAS) and the Association of Banks have published an API playbook. This publication aims to support data exchange between banks and fintech players.

The growing emphasis on introducing regulatory measures to ensure data security will likely drive the adoption of open banking.

Open Banking Sparking a Wave of Innovation in Financial Services by EOS Intelligence

Open Banking Sparking a Wave of Innovation in Financial Services by EOS Intelligence

Open banking is driving innovation in financial solutions

The adoption of open banking is transforming financial solutions, including A2A payments, variable recurring payments (VRP), PFM apps, BaaS, and embedded finance, by enabling faster, more convenient, secure, and personalized financial services.

A2A payments and VRP

Open banking allows secure access to real-time bank data to third-party providers, enabling process automation, speeding up A2A payment transfers, and providing a better user experience. Increasing adoption of open banking globally is expected to make international A2A payments more viable and secure.

Digital wallet platforms such as Apple Pay, Google Pay, and Stripe are looking to integrate open banking on their platforms to provide enhanced user experience. In September 2023, Apple soft-launched a new iPhone wallet app in the UK integrated with an open banking framework to replace traditional banking apps as the preferred platform for accessing information related to their account balance, spending history, etc.

Open banking also encourages the widespread adoption of variable recurring payments by giving consumers more transaction control and transparency. The use of variable recurring payments is expected to increase across various commercial payment services, such as utility bills, subscriptions, and insurance premiums, in the coming years.

PFM apps

Access to financial data enables PFM apps to share more effective and personalized financial advice with consumers. A real-time snapshot of the overall financial health of the consumers helps them make long-term financial decisions.

BaaS

Banking-as-a-service platforms are likely to develop due to the adoption of open banking, allowing non-banking entities to provide financial services without becoming certified banks. This offers consumers a variety of payment and credit options, as well as more personalized finance solutions, expanding the industry offering.

Integrating BaaS in retail is being explored to improve customer loyalty programs and provide seamless payments. Also, the scope of services is likely to expand rapidly, from offering banking services to individual consumers to small and medium-sized enterprises (SMEs) and large corporations in the near future.

Embedded finance

Open banking has become the driving force behind the rise of embedded finance, enabling businesses and corporate clients to enhance operational efficiency and user experience. While retail and e-commerce platforms are some of the first to adopt embedded finance, the adoption is likely to increase in less digitalized spaces such as real estate as well.

Synergy with AI and blockchain offers scope for advanced innovation and security

Open banking provides a data-rich environment by aggregating data from various financial institutions for AI algorithms to analyze and utilize for decision-making. It is expected to benefit AI algorithms further by incorporating new features such as data categorization and anomaly detection in the coming years.

On the other hand, AI is likely to increase the effectiveness of open banking by analyzing individual consumer data and enabling the offering of personalized services. AI and open banking will likely help financial institutions develop innovative products.

While both AI and open banking complement their financial services, they can lead to data misuse or unauthorized access concerns, highlighting the need for strong regulatory measures to keep up with the evolution of open banking and AI.

Blockchain technology will likely become more common in open banking as it will enhance the security and transparency of financial transactions. It will likely reduce the risk of data breaches and unauthorized access to consumers’ finances. Additionally, it will likely make it easier for consumers to share their data by simplifying the authentication and consent processes.

Open banking services have expanded from basic payment initiation to open finance

The open banking framework has evolved from basic account information and payment initiation services to open finance, including access to data from various accounts, including savings, investments, pensions, insurance, and mortgages.

Countries such as India, South Korea, Australia, and Brazil have moved from open banking to open finance to develop a more connected financial ecosystem. In February 2024, South Korea also introduced two initiatives focused on including business data and providing offline open banking services.

In Europe, the European Commission is also pushing towards open finance by introducing the Financial Data Access (FiDA) regulation, a framework to enable secured sharing and access of financial data.

Open banking will diversify consumer options, with non-financial companies such as telecom providers, e-commerce platforms, and utility companies offering innovative financial products. They will likely enter into partnerships with banks to provide integrated services to consumers, enhancing their offerings and creating an interconnected financial ecosystem.

Lack of standardized APIs affects the open banking adoption

While open banking is gaining traction, specific challenges, such as lack of standardized APIs, integration with legacy systems, privacy compliance, and data security, are affecting its adoption.

The lack of standardization of APIs across financial institutions is the key challenge in adopting open banking. Third-party providers are usually unable to adapt to different APIs and provide seamless data sharing between systems.

Various financial institutions also face difficulty integrating open banking into their legacy systems, making the integration process complex and expensive. Banks must first update their systems by investing in technology upgrades and partnering with fintech solutions providers to overcome integration challenges.

As the adoption of open banking increases, the chances of data breaches might also increase, highlighting the need to protect customer data and compliance with privacy regulations. Banks are looking to adopt measures such as encryption, clear usage policies, and regular audits to protect customer data. The European Union has also put regulations such as the General Data Protection Regulation (GDPR) and the Digital Operational Resilience Act (DORA) in place to protect customer data and improve the digital security of financial institutions. Advanced security measures solutions, including tokenization and dedicated API gateways, can also help safeguard customer data.

Lack of awareness among consumers is another key challenge. Users are often unaware of open banking and are reluctant to share their financial data due to privacy concerns. Initiatives aimed at educating the users about security and regulatory norms related to open banking by banks can help overcome this challenge and drive adoption.

EOS Perspective

The shift to an open banking model can transform the future of digital banking. The key driving factors for the users are the ease and clarity of the interface, which are likely to replace the traditional banking infrastructure and ownership of consumer data.

The expected introduction of PSR1 in 2026 will likely improve competition and consumer protection in the payments market, which will likely drive the adoption of open banking. PSR1 will help enhance fraud prevention, improve consumer rights and protection, standardize payment regulations, and enhance open banking functions.

The introduction of regulatory and security measures and growing awareness about open banking and its benefits are also likely to aid this growth. A phased implementation of open banking will help with greater adoption of open banking by gradually introducing the concept to the consumers and helping them adapt.

Open banking will benefit banks by providing better customer insights, encouraging innovation, and creating an additional revenue stream through API monetization. However, increasing competition from fintech and non-financial institutions entering the market will likely pressure banks to transition to open banking. The shift to open finance will further increase the competition in the industry. We will likely witness banks entering partnerships with fintech players to develop and offer innovative financial services for their consumers.

The financial sector is embracing open banking as a means to offer creative and innovative financial solutions to enrich the user experience. Open banking will likely evolve into a broad ecosystem of connected services, streamlining the consumers’ products, services, and applications into one, providing a seamless experience.

NVIDIA's Meteoric Rise Can the AI Chip Giant Sustain Its Dominance by EOS Intelligence
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NVIDIA’s Meteoric Rise: Can the AI Chip Giant Sustain Its Dominance?

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NVIDIA has grown exponentially in recent years. The company made significant strides as an early entrant into the AI chip market, becoming the sector’s leading company. In July 2024, NVIDIA’s market cap was US$2.9 trillion, registering 137.1% growth over 2023, making it the world’s third most valuable company behind Microsoft and Apple. As AI development continues its upward trajectory, big tech companies are focusing on developing their AI capabilities more than ever, posing a threat to NVIDIA’s dominance in the AI chip market.

Over the past decade, NVIDIA has evolved from a gaming GPUs maker to a leader in AI and data centers. The company’s early venture into the computing space coupled with continuous development of its cutting-edge technology helped the company solidify its position as the pioneer in the fast-growing AI training and inference market.

According to Mizuho Securities, a Japanese investment and securities firm, NVIDIA holds 70-95% of the advanced AI chip market share in 2024. Despite being the leading firm and major shareholder in the booming AI chip market, NVIDIA started to face rising competition and regulatory scrutiny that challenge its dominance.

Regulatory scrutiny poses a threat to NVIDIA’s market strategy and dominance

NVIDIA’s dominance has caught the attention of regulators worldwide, with antitrust investigations underway in the USA, EU, and China.

The acquisition of ARM, a UK-based semiconductor company, was scrutinized by regulators in multiple countries and was terminated in 2022. This was due to competition and control of key technology. Qualcomm, Google, and Microsoft opposed the deal because of concerns over fair access to ARM’s technology and fair industry practices.

This increased scrutiny may limit NVIDIA’s ability to offer products and services and impact its strategic expansion plans and market dominance.

NVIDIA's Meteoric Rise Can the AI Chip Giant Sustain Its Dominance by EOS Intelligence

NVIDIA’s Meteoric Rise Can the AI Chip Giant Sustain Its Dominance by EOS Intelligence

Competitors are increasingly vying for NVIDIA’s AI chip market share

The global AI chip revenue is projected to reach US$33.4 billion in 2024, per the Gartner market report, making it a lucrative space to operate in. Major tech companies are investing in AI chip development to compete and break NVIDIA’s monopoly in the market.

Through partnerships, innovation, integrated solutions, and niche offerings, competitors are shaping a competitive landscape that will continue to democratize and push AI tech forward. As the AI computing industry will see unprecedented growth, NVIDIA’s competitors are positioning themselves to capitalize on the emerging opportunities.

Tech companies are investing heavily in their AI chip development capabilities

The generative AI boom has exposed how much the big tech companies depend on NVIDIA. NVIDIA’s biggest customers (Microsoft, Google, Amazon, and Meta Platform), which account for over 40% of its revenue, are building their own AI chips to reduce their dependency on NVIDIA.

Amazon, through AWS, offers its own AI chips, Inferentia and Tranium, as cost-effective alternatives to NVIDIA’s chips. Google has been using its tensor processing units (TPUs) since 2015 and recently announced its Trillium chip. Microsoft is building its own AI accelerators, Maia and Cobalt, and Meta is building its own AI chips for more efficiency.

Among all competitors, Intel is likely to emerge as a core competitor to NVIDIA in the AI chip market, leveraging its experience in making CPUs and GPUs. Intel is challenging the company’s dominance in the AI processor market with the Gaudi accelerator AI chip, which costs one-third of NVIDIA’s GPUs.

Intel is focusing on edge devices, such as smartphones, that utilize smaller language models (LLMs) as part of its “AI everywhere” strategy.

NVIDIA is dominating the fast-growing cloud data center market. Intel’s approach of not replicating NVIDIA’s business model but leveraging its broader technology portfolio is likely to provide it with a competitive edge and a chance to compete with NVIDIA.

AI processing shift to edge devices challenges NVIDIA’s market share

Another challenge for the company is the shift in AI processing from data centers to edge devices such as laptops, PCs, and phones.

Large companies, including Apple and Qualcomm, are updating their chips to run AI models on these devices with neural processors for privacy and speed. Apple’s latest devices are AI optimized, and Qualcomm’s new PC chip allows laptops to run Microsoft AI services on-device.

For NVIDIA, adapting to this new paradigm will be important in the long run. As edge AI grows in demand, the company must innovate and compete in this fast-changing market to remain ahead of the competitors.

Investor-backed startups are making strides in the AI chip market

Many new entrants and growing companies are also competing in the AI chip market with innovative approaches and niche solutions.

Startups, such as Graphcore, Cerebras Systems, Groq, and SambaNova Systems, are building specialized AI architectures to outperform traditional GPUs in specific AI tasks. These startups are backed by strong venture capital and strategic partnerships, providing them with resources to enhance their R&D capabilities and scale much faster. For instance, Grog, a startup in the AI inference market, secured US$640 million and claims to have developed an AI chip faster than NVIDIA’s at a much lower price.

The surge in capital investment is likely to support startups in developing new AI chip solutions and carve out a niche for customized AI workloads. This way, startups can tap into new customers seeking customized chips for specific solutions.

Amidst the competition, NVIDIA is expected to leverage its early head start in the AI chip business and will likely focus on its core strength of developing advanced chips.

NVIDIA’s strategic investment in startups strengthens its robust ecosystem

NVIDIA has created an ecosystem that makes it hard for competitors and customers to switch away. Key components of this ecosystem include strategic investments in startups, software bundling, and partnerships, creating a robust and interconnected web.

NVIDIA’s venture capital arm, NVentures, plays a crucial role in product innovation by investing in startups across various industries.

In addition to financial support, NVIDIA also offers these startups access to its technology and expertise to foster innovation and accelerate product development. For example, NVIDIA Inception, a global program, supports startups by providing technology and marketing support, connecting them with venture capitalists, and giving them access to the latest technical and financial resources.

Investing in promising startups provides NVIDIA with early access to emerging technologies and potential market disruptors. This enables the company to integrate the next big technologies into its products or develop new products that keep it ahead of the competition. It fuels innovation and creates a network of companies that are dependent on NVIDIA’s technology, making it hard for them to switch to competitors.

NVIDIA’s seamless hardware-software integration provides a competitive edge

Software bundling is another way NVIDIA strengthens its ecosystem. The company often bundles its hardware with proprietary software, making its products better and more functional. This software is frequently optimized for NVIDIA’s hardware, so customers cannot switch to competitors without losing access to this software. The strategy of bundling often leads to better performance and value for customers, making NVIDIA’s products more attractive.

NVIDIA’s software ecosystem, particularly CUDA (Compute Unified Device Architecture), plays a vital role in its dominance. CUDA only works with NVIDIA’s chips, and over 3 million developers use it to do AI experiments and develop applications. NVIDIA also updates its software annually with new AI chip architectures and software. The company’s continuous innovation ensures its hardware and software are always in sync, so customers stay within the NVIDIA ecosystem.

NVIDIA’s strategic partnerships enable tech integration across sectors

NVIDIA has partnered with companies ranging from tech giants to startups and helps them develop and optimize their software for their hardware. This has created a network of companies across various industries whose products and services are deeply tied to NVIDIA’s technologies.

NVIDIA’s strategy to form partnerships and integrate them into its network of systems and software is beneficial to both parties. Switching to other competitors would incur significant costs and disruptions for customers. NVIDIA’s industry-wide partnerships help it have a strong and integrated ecosystem. For example, partnerships with AWS, Microsoft Azure, and Google Cloud allow NVIDIA to integrate GPUs into the cloud and make their technology available to all enterprises and developers.

In the automotive space, partnerships with Tesla and Mercedes-Benz put NVIDIA’s AI and GPU into autonomous driving, making them rely on NVIDIA AI solutions. Further, partnerships with large enterprises, such as IBM and VMware, to optimize hardware and software make NVIDIA the preferred partner for advanced computing in data centers and AI applications.

NVIDIA’s dominance may lead to increased costs of manufacturing AI chips

NVIDIA’s dominance is likely to significantly impact the world’s largest contract chip maker, TSMC (Taiwan Semiconductor Manufacturing Company), and the entire semiconductor industry.

NVIDIA is TSMC’s key customer, and the latter dedicates a big part of its production capacity to NVIDIA. NVIDIA’s pricing power impacts TSMC’s margins, and if NVIDIA decides to squeeze its suppliers to maintain its margins, TSMC is likely to feel the heat on its profitability. This could lead to capacity constraints for other customers, which will delay their product launches and drive up the prices of AI chips.

An increasing demand for AI chips from NVIDIA and others will drive up the cost of raw materials and components. This cost increase may trickle down the supply chain to end consumers. NVIDIA’s dependence on TSMC makes the supply chain vulnerable to disruptions due to China’s multi-pronged pressure on Taiwan.
NVIDIA’s dominance could drive consolidation in the semiconductor industry

NVIDIA and other companies may diversify their supply chain to mitigate the risks associated with geopolitics, supply, demand, and prices. This could lead to partnering with multiple foundries and geographic diversification. Some semiconductor companies may go for vertical integration to have more control over the value chain.

NVIDIA’s dominance and financial muscle may lead to consolidation in the semiconductor industry. Companies lacking financial resources may find it challenging to compete with big tech companies and could potentially get acquired by larger AI chip manufacturing companies.

Companies in the automotive and electronics sectors that rely on semiconductors may face procurement challenges due to supply shortages. This may lead to prioritizing high-margin products and potentially disrupting the availability of lower-margin products.

EOS Perspective

Only a limited number of global players operate in the AI chip manufacturing space, with NVIDIA holding the majority share. Startups and big tech companies are building strategies to carve out their market share.

NVIDIA will likely hold on to its market leadership with a slight dip in market share to core competitors, such as Intel and AMD, in the next few years. However, with its investments in AI R&R and its initiatives to diversify into different segments, NVIDIA might have a chance to recapture lost market share and grab new growth opportunities in the long term.

As the competition in the AI chip market intensifies, we can expect the launch of more affordable AI chips from NVIDIA competitors designed for customized AI applications. NVIDIA, on the other hand, would prioritize performance and reduce the cost of its AI chips. Since the competitors still lag in designing and developing advanced AI chips and often depend on third parties, NVIDIA is likely to capitalize and dominate the high-performance AI chip space.

With the massive and growing AI market, there is plenty of room for competitors and startups to grow even with a small market share. However, regulatory delays, sustainability issues, and unethical AI use can block strategic initiatives, increase the cost of compliance, and create uncertainty for investors and partners. Navigating these challenges will make NVIDIA more resilient and agile. The increased transparency and compliance can open up new partnership opportunities and new markets in regions where compliance is a major concern.

As AI will be the source of value for many businesses, NVIDIA will use its position to diversify by tapping into new markets to reduce its dependence on traditional markets. A potential partnership the company is discussing with OpenAI, a US-based AI research organization, will likely create a pool of new commercial opportunities for both companies to explore and monetize AI-driven solutions in the healthcare, finance, and automotive sectors.

 

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