Medical device companies and digital health founders that are developing and deploying healthcare artificial intelligence (AI) tools face a recurring product-design conflict. The strongest business case is usually an improving model, one that learns from new clinical data over time. However, a “locked” or “fixed” model that does not automatically learn or adapt after deployment may raise fewer regulatory concerns than one that continuously changes based on new data. And the data architecture needed to bridge that gap runs directly into considerations over federal laws restricting release of medical information.
Three legal regimes — patent strategy, U.S. Food and Drug Administration regulation, and HIPAA-driven patient privacy obligations — are pulling in different directions, and the developers that navigate them well will be the ones that plan for all three from the start.
Patentability: Technical specificity matters more than adaptability
It is tempting to assume that a continuously learning model is inherently more patentable than a static one. That framing is too simple. The more important question under current case law is whether the claimed invention is presented as a specific technological application or improvement, not as an abstract mathematical concept.
A claim directed broadly to “using a trained model to make a medical prediction” (i.e., AI as a “black box”) is exposed to an eligibility challenge. A claim tied to a particular data-processing pipeline, model architecture deployed in a defined clinical setting, or a measurable improvement in system performance stands on firmer ground. What strengthens the patent narrative is not that the model changes over time, but that the claims describe an engineered solution to a defined healthcare problem using defined technical means.
A learning-capable system can support that narrative. It might claim a specific retraining protocol, a drift-detection mechanism, or a feedback loop tied to clinical-outcome data. But the patent value comes from the engineering, not the abstraction.
FDA: Fixed models remain simpler submissions
If a healthcare AI tool is intended to diagnose, cure, mitigate, treat, or prevent disease, then the FDA will regulate it as a medical device. From a submission standpoint, a locked model is preferred by the FDA as a more straightforward pathway. These fixed models let the manufacturer define the exact version under review, the training-data boundaries, the validation methodology, and the performance benchmarks. That aligns with the FDA’s core interest: demonstrating safety and effectiveness at the time of clearance or authorization.
The tension is that product teams want the model to improve as new hospital, clinic, and patient data arrives. But a model that updates itself in the field can undermine the stability a 510(k) or De Novo submission is meant to demonstrate.
This does not mean the model must remain permanently frozen. The FDA’s Predetermined Change Control Plan (PCCP) framework is designed to accommodate post-market modifications, but only within anticipated, bounded, and validated limits. The manufacturer defines in advance what types of changes the model may undergo, under what conditions, and with what evidence. Open-ended self-improvement is not what the framework contemplates. Managed evolution is.
The practical takeaway for medical device companies: build the submission around a locked baseline and use the PCCP or a comparable change-control process to create a documented pathway for future updates.
HIPAA: Ongoing learning depends on lawful data pipeline
The Health Insurance Portability and Accountability Act (HIPAA) is where product ambition meets operational reality. HIPAA is a U.S. law that sets national standards for protecting sensitive patient health information. It regulates how healthcare providers, insurers, and their business associates use, store, and share protected health information (PHI), and requires safeguards to ensure its privacy and security. A medical device company developing an AI tool that wants to monitor drift, retrain the model, or validate performance over time may require ongoing access to real-world patient data. If that data qualifies as PHI and the company is creating, receiving, maintaining, or transmitting it on behalf of a covered entity as part of providing a service to the covered entity, the company is likely functioning as a business associate under HIPAA.
That status carries concrete obligations. The parties should have a business associate agreement in place, and the agreement should expressly define any permitted uses of PHI for model improvement, rather than treating them as incidental to the service. HIPAA security rule compliance also becomes central, particularly where the system depends on cloud infrastructure, downstream subcontractors, or shared computing environments.
If the company hopes to reduce HIPAA exposure by working with de-identified data, it still needs a defensible de-identification strategy under one of the two recognized methods: Safe harbor (removing 18 specified identifiers) or expert determination (a qualified statistician certifying that re-identification risk is very small). A casual assumption that the data is “anonymous” is not a defensible strategy.
The result of these patient data privacy implications is a hidden legal dependency beneath the “improving model” thesis. Without a durable, lawful data-rights structure that supports post-market learning, the model may be theoretically improvable but practically fixed, locked not by design choice but by compliance gaps.
Final takeaways for medical device companies
Patent law pushes the company to describe a concrete technical innovation. FDA regulation pushes the company to bound and validate that innovation at a point in time. HIPAA pushes the company to govern the data stream that fuels innovation, but only within the limits of the law.
The practical answer is not to choose between a fixed AI model and a learning model in the abstract, but to design the product in phases. Lock the initial commercial release tightly enough for a defensible regulatory submission. Focus the patent strategy on technical implementation and clinical application, not abstract prediction claims. And build the data architecture from day one to support lawful monitoring, carefully scoped retraining, and documented change control.
In healthcare AI, the winning product may not be the model that learns the fastest. It may be the one designed from the outset to evolve in a careful, lawful, and documentable manner.
Photo by H. Armstrong Roberts/ClassicStock/Getty Images
Robert Botkin helps clients of all sizes, from Fortune 50 companies to startups, navigate their legal needs tied to privacy, cybersecurity, artificial intelligence (AI), and machine learning. He is the leader of Parker Poe’s AI Services Team and is known for his ability to translate complex technical concepts and issues into actionable tasks that clients can use to establish effective governance programs in a rapidly changing legal landscape.
Clients turn to Marci Norton to navigate the complex statutes and regulations administered by the U.S. Food and Drug Administration. She provides federal regulatory and compliance advice to a range of companies across the healthcare, life sciences, and food and beverage industries, bringing a unique perspective after nearly 30 years in the FDA’s Office of the Chief Counsel (OCC).
Tim St. Clair helps clients solve complex patent and other intellectual property problems with practical judgment shaped by unusual breadth of experience. Most patent lawyers are either litigators or prosecutors. Tim has built his practice doing both, along with substantial opinion work. He helps clients focus on what matters, tune out patent lawyer noise, and address disputes and strategy questions with the right level of urgency, investment, and precision.
Olivia Osburn advises corporate healthcare clients on a wide range of regulatory, compliance, and transactional priorities, with an emphasis on mergers, acquisitions, joint ventures, corporate governance, and artificial intelligence (AI). She works closely with physician groups and other healthcare organizations to advise on operational, structural, and legal needs that arise across the healthcare industry, including practice management guidance, contract structuring, and long-term strategic planning.
Caroline McCracken focuses her practice on complex business litigation in state and federal courts. She handles a variety of commercial disputes, including breach of contract, breach of fiduciary duty, unfair trade practices, arbitration agreements, and consumer protection claims. She has experience in multidistrict litigation (MDL) concerning PFAS claims. She also defends nonprofit organizations in litigation involving novel questions under South Carolina’s Nonprofit Corporation Act.
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