For much of the last decade, healthcare AI has been evaluated like a technology story. Investors focused on model performance. Founders competed on accuracy. Health systems asked whether an algorithm could outperform existing workflows.
Those factors still matter. But they are becoming less differentiating.
Open-source models, foundation models, and increasingly accessible computing infrastructure have lowered the barriers to building AI. As a result, the next generation of healthcare AI winners will be determined by something far less discussed: their ability to navigate regulation, generate evidence, secure reimbursement, and earn trust.
The most durable competitive advantage in healthcare AI is not technical sophistication. It is regulatory sophistication.
Why Regulation Is Becoming a Competitive Advantage
1. Healthcare AI Companies Are Becoming Regulatory Businesses
The FDA has authorized more than 1,500 AI-enabled medical devices as of early 2026, up from roughly 1,250 a year earlier. That’s a pace of more than 70 new clearances per quarter (FDA AI-Enabled Medical Devices List).
Unlike traditional medical devices, many AI systems evolve after they reach the market. Regulators are responding by building frameworks that let products improve post-approval while maintaining safety and effectiveness. The FDA’s Predetermined Change Control Plan (PCCP), finalized in December 2024, lets manufacturers pre-specify how an AI device is allowed to change (new training data, performance updates, expanded use) without filing a new marketing submission for each change (FDA PCCP Guidance).
That’s a structural advantage, not just a paperwork detail. A company with an FDA-cleared PCCP can keep iterating on its model in the field; a competitor without one has to file a new submission every time. The winner won’t be whoever has the best algorithm today. It’ll be whoever built a regulatory pathway that lets the algorithm keep getting better.
2. The Most Valuable Data May Be Created After Commercialization
Much of the discussion around healthcare AI focuses on training data. Investors may be underestimating the value of deployment data and overestimating how well-supported today’s cleared devices actually are.
A JAMA Network Open analysis of 903 FDA-authorized AI devices (cleared through August 2024) found that only 55.9% had publicly available clinical performance data at the time of clearance. Devices missing that data carried a measurably higher recall risk: 43 of the 903 devices (4.8%) were recalled, at an average of 1.2 years post-approval, and missing clinical evidence was one of the strongest predictors of which devices got recalled.
A couple of things follow from that. Clearance isn’t the same as proof: a lot of the market is running on thin evidence, and that’s actually an opening for a company with better data. And once a device is out in the field generating real outcomes, it starts closing that evidence gap in a way competitors can’t easily copy. Companies that treat post-market evidence generation as a core part of the product, not an afterthought, end up with a data asset rivals can’t just buy their way into.
3. Governance and Reimbursement Are Becoming Growth Drivers
Healthcare organizations are not simply buying software. They are adopting systems that influence clinical decisions and patient outcomes, and they scrutinize transparency, validation, accountability, and bias mitigation accordingly.
But the bigger barrier is economic. A NEJM AI analysis of medical AI adoption using U.S. insurance claims data found that usage remains highly concentrated. As of the study period, only two device categories (coronary artery disease assessment and diabetic retinopathy screening) had crossed 10,000 billed claims nationally, and most cleared AI devices had barely any claims volume at all. Adoption also skewed sharply toward higher-income, metropolitan zip codes with academic medical centers.
Most FDA clearance, in short, doesn’t translate into real usage. The gap between “cleared” and “reimbursed and adopted at scale” is where most healthcare AI companies are currently stuck, and it’s a gap that clinical validation alone won’t close. Economic validation, a defined reimbursement pathway, and workflow integration matter just as much as accuracy.
What This Means for Investors
We increasingly evaluate healthcare AI companies through a lens broader than technical performance. A compelling demo or a strong accuracy metric earns attention, but it rarely builds a durable company. The adoption numbers above show why.
The questions we ask:
- Does the company have a credible regulatory strategy, including a plan for post-market change (e.g., a PCCP)?
- How will it generate the clinical evidence that more than 40% of its FDA-cleared peers currently lack?
- What is the specific pathway to reimbursement, and can it point to CPT code alignment or payer conversations already underway?
- How will it manage governance, transparency, and liability as the system scales?
- What proprietary data or workflow advantage will exist once the product is deployed that a fast-follower can’t replicate?
Viz.ai, a company outside BioAdvance’s portfolio, illustrates the pattern well. Its stroke-detection algorithm received FDA de novo clearance in 2018, but the more consequential step came in September 2020, when it became the first AI software to receive a Medicare New Technology Add-on Payment (NTAP), a reimbursement pathway that CMS had not previously extended to AI. Other AI vendors were evaluated for the same payment; Viz.ai was the one whose renewal was approved, on the strength of demonstrated clinical outcomes paired with a reimbursement strategy built alongside the product from day one. By the time the NTAP was in place, Viz.ai was already deployed in hundreds of hospitals nationwide (Viz.ai, CMS NTAP renewal announcement). That combination, real clinical evidence plus a regulatory and reimbursement pathway built early, is what let it pull ahead of technically comparable competitors.
Anyone can build a model. Far fewer organizations can generate evidence, navigate FDA oversight, earn provider trust, secure reimbursement, and sustain adoption over time. Those capabilities are what will separate the companies that get cited in a JAMA study for their evidence gaps from the ones that get reimbursed at scale.
Looking Ahead
The healthcare AI companies that define the next decade will not be the ones with the most impressive demos. They will be the organizations that understand how innovation, regulation, reimbursement, and trust interact within one of the world’s most complex industries.
The market continues to focus heavily on model performance. We believe investors should weigh regulatory capability just as heavily.
In healthcare AI, regulation is no longer simply a hurdle to overcome. It is becoming the moat.
If you’re building a healthcare AI company in the mid-Atlantic region of the U.S., where regulatory strategy is core to the product, not an afterthought, we’d like to hear from you.
Sources
FDA. “Artificial Intelligence-Enabled Medical Devices.” fda.gov
FDA. “Marketing Submission Recommendations for a Predetermined Change Control Plan for Artificial Intelligence-Enabled Device Software Functions.” Final guidance, December 2024. fda.gov
JAMA Network Open. “Generalizability of FDA-Approved AI-Enabled Medical Devices for Clinical Use,” 2025. jamanetwork.com
NEJM AI. “Characterizing the Clinical Adoption of Medical AI Devices through U.S. Insurance Claims,” 2023. ai.nejm.org
Viz.ai. “Viz.ai Receives New Technology Add-on Payment (NTAP) Renewal for Stroke AI Software from CMS,” August 2021 (not a BioAdvance portfolio company; cited as an outside industry example). viz.ai