Beyond the Algorithm: How Lifecycle Regulation Is Building Entry Barriers and Concentrating the Healthcare AI Market

CPI

The U.S. Food and Drug Administration is building a new regulatory framework for artificial intelligence and machine learning in medical software. This article argues that the FDA’s evolving approach extends beyond a compliance issue for technology developers; it is a market-shaping force that will change how healthcare providers buy, deploy, and govern AI-enabled tools.

Drawing on recent FDA guidance, academic research, and industry analysis, this study examines four downstream effects: shifting procurement criteria that favor regulatory-ready vendors, unresolved liability questions when adaptive algorithms cause harm, accelerating market consolidation among device makers, and the growing burden of post-market surveillance. For competition policy observers, the convergence of healthcare regulation and technology markets presents new questions regarding barriers to entry, platform power, and the structural consequences of compliance costs.

I. Introduction

Artificial intelligence is no longer a speculative feature of the future of healthcare; it is a present-tense reality embedded in radiology workstations, clinical decision-support systems, and patient triage tools across the United States. As of the end of 2025, the FDA had authorized approximately 1,450 AI/ML-enabled medical devices, a figure that has roughly doubled every two to three years since the agency began tracking them.[2] The market for AI-enabled medical devices was valued at roughly $13 to $19 billion in 2024 and is projected by one widely cited analyst to reach approximately $256 billion by 2033, implying a compound annual growth rate of approximately 38 percent.[3]

The conversation about healthcare AI tends to focus on the technology itself: what the algorithms can do, how accurate they are, and which clinical problems they can solve. The regulatory architecture surrounding these tools receives less discussion, yet it is arguably more consequential for the structure of the healthcare market. The FDA’s evolving framework for AI/ML-based Software as a Medical Device (“SaMD”) is not merely a set of compliance rules for developers. It is an active force reshaping the competitive landscape, changing who sells AI to hospitals, how hospitals select vendors, and who bears responsibility within a health system when an algorithm produces an error.

This study examines the downstream market effects of the FDA’s regulatory evolution. It targets competition policy professionals, legal advisors, and healthcare executives who need to understand how regulation is restructuring the provider market. The thesis is straightforward: the FDA’s shift from static, one-time device approvals to a lifecycle-based regulatory model raises the cost and complexity of bringing AI products to market, concentrates power among a smaller number of well-capitalized vendors, and forces healthcare providers to build entirely new internal capabilities to manage these tools.

This article divides into five sections. It first traces the regulatory shift from static to adaptive oversight. It then examines how this shift changes vendor procurement, complicates liability, accelerates market consolidation, and expands the post-market surveillance burden. Each section draws on verifiable data from published sources.

II. From Static to Adaptive: The FDA’s Regulatory Evolution

The FDA’s traditional model for regulating medical devices applied to products that do not change after marketing. Once approved, a hip implant remains unchanged. AI/ML software, however, can learn, adapt, and modify its behavior based on new data. This fundamental characteristic breaks the traditional regulatory paradigm.

The agency recognized this problem as early as 2019, when it published a discussion paper proposing a new framework for modifications to AI/ML-based SaMD.[4] In January 2021, the FDA released its AI/ML SaMD Action Plan, committing to five priorities: a tailored regulatory framework, good machine learning practices, patient-centered transparency, methods for addressing algorithmic bias, and real-world performance monitoring.[5]

The most significant regulatory innovation to emerge from this process is the Predetermined Change Control Plan (“PCCP”). The FDA finalized the PCCP guidance in December 2024, allowing manufacturers to describe, in their initial marketing submission, the types of modifications they anticipate making to their AI software after deployment, along with the methodology for validating those changes.[6] If approved, manufacturers may implement these pre-authorized changes without filing a new premarket submission each time. This departure from the previous model is meaningful. It acknowledges that AI products are living systems rather than static artifacts.

Alongside PCCPs, the FDA has promoted Good Machine Learning Practices (“GMLP”), a set of ten guiding principles that the FDA developed jointly with Health Canada and the United Kingdom’s Medicines and Healthcare products Regulatory Agency in October 2021.[7] These principles address data quality, model transparency, clinical validation, and performance monitoring. Although not legally binding, they signal the standard of care the FDA expects. The FDA’s goal is a comprehensive framework ensuring that AI technologies meet safety and efficacy benchmarks while respecting patient rights and equitable access.[8]

Gerke, Babic, Evgeniou & Cohen argued in npj Digital Medicine that the FDA must look beyond individual devices and evaluate entire systems. This includes how AI tools interact with clinical workflows, human decision-making, and patient populations.[9] Regulators have not yet fully adopted this “system view,” but it frames the challenge: regulating AI in healthcare involves more than the algorithm itself. It requires evaluating the ecosystem in which the algorithm operates.

III. How Regulation Is Reshaping Procurement and Vendor Selection

The regulatory shift described above already changes how hospitals and health systems purchase AI technology. Historically, clinical performance, price, and interoperability with existing IT systems drove procurement decisions for clinical software. Today, regulatory compliance increasingly serves as a threshold requirement.

Industry survey data indicate that Chief Medical Information Officers and Chief Information Officers are increasingly routing vendor decisions through formal AI governance structures, elevating compliance, transparency, and oversight alongside clinical performance. A December 2025 CHIME Foundation survey found that 84 percent of responding healthcare organizations had established an AI governance committee, with CIOs serving on 63 percent of these bodies and CMIOs on 45 percent. The practical articulation of this emerging standard arrived earlier, in September 2025, when the Joint Commission and the Coalition for Health AI jointly issued guidance on the responsible use of AI in healthcare. This guidance sets out seven core elements, including AI policies and governance structures, data security, ongoing quality monitoring, and bias assessment. Industry checklists and evaluation frameworks routinely include questions about GMLP adherence, post-market surveillance capabilities, and bias mitigation strategies.

This dynamic creates a competitive environment worth monitoring. Vendors who can demonstrate a clear regulatory track record, robust PCCP, and mature quality management system hold a significant advantage. Startups and smaller innovators face a disadvantage if they cannot demonstrate institutional capacity for regulatory compliance, even when they possess superior algorithms. Industry analyses estimate that digital health startups should plan for $75,000 to $250,000 in baseline regulatory and security costs, with expenditure exceeding $500,000 where FDA clearance is involved. For a company at the seed or Series A stage, this amount can constitute a prohibitive barrier to entry.

The practical result is that procurement has become a filtering mechanism. Health systems do not simply select the best products; they select the vendor most likely to remain in good regulatory standing over the product lifecycle. This approach favors incumbents and platform players with deep compliance infrastructure.

A systematic review by Ahmed & colleagues identified privacy, trust, transparency, accountability, and physician buy-in as the core obstacles to AI adoption. Each of these barriers feeds directly into procurement criteria. The more demanding these criteria become, the fewer vendors can clear them successfully. This dynamic has significant implications for competition policy.

IV. Liability in the Age of Adaptive Algorithms

When an adaptive AI algorithm contributes to a misdiagnosis or poor treatment recommendation, who bears responsibility? The developer who trained the model? The hospital that deployed it? The physician who relied on its output? As of early 2026, the answer remains uncertain.

A systematic review by Cestonaro & colleagues in Frontiers in Medicine concluded bluntly that the regulatory framework for medical liability when AI is applied to diagnostics is “inadequate and requires urgent intervention.” The authors found no single regulation governing the liability of the various parties in the AI supply chain.[10] Maliha, Gerke, Cohen & Parikh noted in the Milbank Quarterly that developers could face product liability claims for poor design or failure to warn, but that existing legal frameworks were not built with adaptive algorithms in mind.[11]

On the provider side, Lee & colleagues argued in Biomedical Instrumentation and Technology that general tort law principles still hold clinicians liable for medical malpractice, even when they rely on AI recommendations in good faith.[12] The resulting asymmetry creates a notable imbalance: manufacturers remain insulated by the learned-intermediary doctrine while clinicians bear frontline malpractice exposure. This has prompted calls from provider organizations to reallocate risk toward device makers, though no federal statutory fix has yet advanced.

The adaptive nature of many AI products adds another layer of complexity. When a locked algorithm causes harm, investigators can examine the product’s behavior at the time of injury. When an adaptive algorithm causes harm, the relevant questions multiply: what version of the model was running, what data influenced that version, and whether the modification was covered under an approved PCCP. The malpractice system has not previously encountered this level of forensic complexity.

Liability ambiguity has practical consequences for the market. Vendors with deeper pockets and stronger legal teams can manage litigation risks more effectively. Health systems must invest in new risk management protocols, revisit malpractice insurance arrangements, and build internal documentation practices for AI-assisted clinical decisions. The absence of clear liability rules does not freeze market dynamics; rather, it tilts the playing field toward larger and more established actors on both the vendor and provider sides.

V. Market Consolidation and the Competition Question

Regulatory compliance costs, liability exposure, and organizational demands for AI governance all point in the same direction: consolidation. The healthcare AI market is growing rapidly, but the benefits of this growth may accrue disproportionately to a small number of large players.

Investment patterns already reflect this trend. Rock Health’s 2025 year-end digital health funding report found that AI-enabled digital health companies captured 54 percent of total funding, up from 37 percent the prior year, and commanded roughly a 19 percent premium on average deal size. Mega-deals above $100 million accounted for 42 percent of all funding, the highest share since 2021. Removing the top nine companies by 2025 dollars raised causes total funding to fall below 2024 levels, indicating concentration among top-tier players.[13] This represents more than capital flowing into the sector; it is capital concentrating in the largest firms.

The electronic health record market offers a useful parallel. Epic and Oracle Health (formerly Cerner) now collectively hold more than half of the hospital EHR market, and Epic has crossed 40 percent of acute care hospital market share based on KLAS Research data.[14] Holmgren & colleagues documented this consolidation in a 2025 peer-reviewed study in Health Affairs Scholar. They noted that health systems have increasingly clustered around the two largest EHR vendors.[15]

Both Epic and Oracle Health have embedded AI capabilities directly into their EHR platforms. For hospitals already locked into one of these ecosystems, the path of least resistance is to adopt the AI tools offered by their existing vendor rather than to integrate a third-party product. This dynamic reflects the familiar pattern of platform economics: once a provider commits to a platform, switching costs make it rational to stay, even if competing products offer technical superiority. The regulatory burden compounds this effect. Integrating a new AI vendor requires additional compliance work, new vendor agreements, and new governance protocols. The easier choice involves less friction.

The broader healthcare provider market is also consolidating. The Bipartisan Policy Center documented 1,573 hospital mergers from 1998 to 2017, followed by another 428 mergers between 2018 and 2023.[16] As health systems grow larger, their procurement power increases, and their preference for integrated, compliance-ready platforms intensifies. This creates a self-reinforcing cycle: consolidation among providers drives consolidation among vendors, raising barriers to entry for new competitors.

Adoption data tell a nuanced story. A 2025 cross-sectional study in JAMA Health Forum using the U.S. Census Bureau’s Business Trends and Outlook Survey found that healthcare AI use rose over the period but remained below that of most other industries.[17] (extending the BTOS analysis through the end of 2025 and noting a November 2025 reframing of the survey’s AI-use question). Meanwhile, the Office of the Assistant Secretary for Technology Policy reported that adoption of predictive AI in U.S. hospitals rose from 66 percent in 2023 to 71 percent in 2024, with significant disparities: 86 percent of system-affiliated hospitals used predictive AI compared with 37 percent of independents, and 96 percent of large hospitals compared with 59 percent of small hospitals.[18] These disparities confirm that AI adoption concentrates in larger hospital systems. Menlo Ventures found that health systems led adoption, materially ahead of outpatient providers and payers.[19] The adoption curve, like the investment curve, tilts toward scale.

A counterargument is worth noting, however. Some analysts predict that high compliance costs could create a new market for specialized “AI compliance-as-a-service” firms that help smaller developers navigate the regulatory landscape. Whether this opportunity materializes quickly enough to counteract the consolidation trend remains an open question.

VI. Post-Market Surveillance: The New Compliance Frontier

Perhaps the most consequential regulatory development is the FDA’s increasing emphasis on what happens after a device reaches the market. For traditional medical devices, post-market surveillance is largely a matter of tracking adverse events through the Medical Device Reporting system. For AI/ML devices, the challenge is fundamentally different because the device itself can change.

The FDA’s Office of Science and Engineering Laboratories has invested in developing tools to detect changes in AI inputs, monitor output performance, and identify the causes of performance degradation. On September 30, 2025, the FDA issued a Request for Public Comment seeking input on practical approaches to measuring and evaluating the performance of AI-enabled medical devices in the real world. The request also sought strategies for detecting, assessing, and mitigating performance changes over time.[20] Comments closed December 1, 2025, and stakeholders responded with divided views on the appropriate scope and methodology for ongoing monitoring.

Feng & colleagues have argued that stakeholders should continuously monitor and update AI algorithms, drawing on the principles of clinical quality improvement rather than the one-time validation of traditional devices.[21] Babic & colleagues have proposed a general governance framework for marketed AI/ML devices. They noted that the FDA’s Medical Device Reporting system was not originally designed to address the challenges of algorithmic monitoring.

The concept of “model drift” illustrates this problem. An AI algorithm trained on data from one patient population may perform differently when a hospital serving a demographically distinct community deploys it. Over time, the data environment shifts, and the model’s accuracy can degrade without anyone noticing unless a robust monitoring system is in place. This dynamic places a continuous, open-ended compliance obligation on both developers and deployers of AI systems. Passing the initial regulatory review is not sufficient; the product must remain safe and effective for as long as it is used. The resource burden is substantial and falls more heavily on organizations that lack dedicated monitoring infrastructure.

A related concern is the pressure that market incentives exert on safety. A 2025 cross-sectional study in JAMA Health Forum examined all FDA-cleared AI-enabled medical devices through November 2024. The study found that publicly traded firms accounted for just over half of the devices on the market but were associated with more than 90 percent of recall events. Approximately 43 percent of those recalls occurred within the first year of clearance, about twice the rate observed for all 510(k) devices.[22] This finding suggests that investor pressure to ship products quickly can sit in tension with thorough post-market safety practices. For smaller developers lacking the engineering resources to build real-time monitoring dashboards, the continuous surveillance obligation represents yet another cost advantage for larger competitors.

VII. Looking Ahead

The FDA’s regulatory framework for AI/ML medical software remains under development. The agency is finalizing guidance documents, testing PCCPs in practice, and building post-market surveillance infrastructure in real time. The market-shaping effects of this regulatory evolution, however, are already visible.

Healthcare providers are changing their vendor selection criteria, favoring regulatory maturity over algorithmic novelty. New governance roles and frameworks are emerging within health systems, creating organizational demands that vary sharply by institution size and resources. The liability landscape remains unsettled, creating risks for both providers and developers. Investment capital flows toward the largest players, while compliance costs raise barriers to entry for smaller competitors. The post-market surveillance regime imposes ongoing obligations that require continuous investment in monitoring infrastructure.

These trends deserve the attention of competition policy professionals. The regulatory framework does not merely protect patients; it structures markets. The choices regulators make now will determine whether the healthcare AI market evolves into a competitive ecosystem of diverse innovators or a concentrated oligopoly dominated by a handful of platform players. These choices include how strictly to enforce GMLP, how to define the scope of PCCPs, how to allocate liability between developers and deployers, and how to fund post-market surveillance.

The stakes are not hypothetical. Nair & colleagues documented in PLOS ONE that the complexity and diversity of healthcare systems presents the primary challenge for AI implementation.[23] Poon & colleagues surveyed health system leaders and found that AI priorities, successes, and challenges varied widely across institutions. This variation makes a one-size-fits-all regulatory approach difficult to calibrate.[24] Regulators, policymakers, and competition enforcers should examine these structural dynamics now, while the framework is still taking shape, rather than after the market has already locked in.

The FDA’s stated goal is to protect patients while enabling innovation. Achieving both requires a regulatory design that attends to competitive dynamics, not just clinical safety. The algorithm is only part of the story. The regulatory architecture surrounding it may prove equally important.

Originally published by CPI.

[1] Douglas Grimm, FACHE, is a Partner and Health Care Practice Group Leader for the law firm ArentFox Schiff in Washington DC. Douglas McCormack, JD, is Founder and Managing Partner of Acumen Partners, LLC, a Washington, D.C. healthcare investment and advisory firm that works with medtech companies and clinical practices, including on health AI implementation.

[2] FDA, AI-Enabled Medical Devices List.

[3] Grand View Research, AI-Enabled Medical Devices Market Size & Trends (2025).

[4] U.S. Food & Drug Admin., Proposed Regulatory Framework for Modifications to AI/ML-Based Software as a Medical Device: Discussion Paper and Request for Feedback (2019).

[5] U.S. Food & Drug Admin., AI/ML SaMD Action Plan (2021).

[6] U.S. Food & Drug Admin., Marketing Submission Recommendations for a Predetermined Change Control Plan for Artificial Intelligence/Machine Learning (AI/ML)-Enabled Device Software Functions (Dec. 2024).

[7] U.S. Food & Drug Admin., Good Machine Learning Practice for Medical Device Development: Guiding Principles (2021).

[8] Haider J. Warraich, Troy Tazbaz & Robert M. Califf, FDA Perspective on the Regulation of Artificial Intelligence in Health Care and Biomedicine, 333 JAMA 241 (2025).

[9] Sara Gerke et al., The Need for a System View to Regulate Artificial Intelligence/Machine Learning-Based Software as Medical Device, 3 npj Digital Med. 53 (2020).

[10] Clara Cestonaro, Arianna Delicati, Beatrice Marcante, Luciana Caenazzo & Pamela Tozzo, Defining Medical Liability When Artificial Intelligence Is Applied on Diagnostic Algorithms: A Systematic Review, 10 Front. Med. 1305756 (2023).

[11] George Maliha, Sara Gerke, I. Glenn Cohen & Ravi B. Parikh, Artificial Intelligence and Liability in Medicine: Balancing Safety and Innovation, 99 Milbank Q. 629 (2021).

[12] Brian Lee et al., Liability Exposure of Clinicians in Artificial Intelligence-Driven Healthcare, 58(2) Biomed. Instrumentation & Tech. 39 (2024).

[13] Rock Health, 2025 Year-End Digital Health Funding Overview: A Tale of Two Markets (Jan. 2026).