November 11, 2026 (Inside AI) — The U.S. Food and Drug Administration cleared 295 AI/ML-enabled medical devices in 2025, and that number is expected to rise. But as strategic buyers chase these assets, they are discovering that the deal terms for AI-powered devices look nothing like those for traditional medical equipment. Data ownership, algorithm drift, interoperability dependencies, and a crowded patent landscape are forcing acquirers to rewrite their playbooks.
Legal experts at Troutman Pepper Locke LLP argue that the unique characteristics of AI-enabled devices demand new approaches to diligence and deal structuring. In a recent commentary, partners Douglas Gray, Joseph Kadlec, and Rusty Close outlined the challenges and offered practical solutions for buyers navigating this evolving terrain.
The core problem is that AI devices are not static products. They learn and adapt, often pulling in new data from clinical workflows long after the deal closes. That dynamism creates risks that standard representations and warranties may not cover.
Who Really Owns the Training Data?
One of the primary challenges is that data ownership and control may not extend to the underlying data the device algorithm trains on or interacts with. This is especially true for devices integrated into clinical workflows that continuously receive new data from new sources. When new data is introduced post-closing, whether for training or device operation, new data sources, inaccuracies, or changes can increase the risk of model drift or impact algorithm performance.
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Diligence must therefore focus on the chain of title for training data. Depending on the scope of use permitted for licensed data, there may be limitations on future uses, or change of ownership provisions within existing licenses. Existing protocols for detecting and addressing performance drift should also be evaluated, as well as whether training methods and outcomes fall within a device's 510(k) clearance from the FDA or would trigger new submissions.
Patient consent, authorization, and de-identification compliance are core considerations. Medical devices with clinical applications will inevitably access and potentially use patient data. Diligence should focus on proper uses and disclosures under HIPAA and state privacy and consumer health data protection laws.
The legal documents should include representations regarding prior, existing, and ongoing data rights, as well as indemnities protecting the buyer with respect to lawful use and sourcing of incoming data. The parties may also wish to allocate cost responsibilities for any data drift incidents post-closing. Additionally, buyers may consider structuring a portion of consideration as an earnout tied to the device's continued data access rights and algorithm performance benchmarks for a defined period.
This approach mirrors trends in other tech sectors where earnouts have become common to bridge valuation gaps. But in health care, the stakes are higher because patient safety and regulatory compliance are on the line. A data drift incident could not only degrade performance but also trigger FDA scrutiny or privacy violations.
External Dependencies Multiply the Risk
AI-enabled medical devices often must interoperate with systems the target company does not own or control, such as electronic health record (EHR) integrations, cloud hosting, and application program interfaces (APIs). These relationships are essential to the device's ongoing performance and functionality. Each dependency can raise distinct diligence issues, including consent, assignment, service levels, continuity, information security, pricing, data access, field-of-use or data retention limits, exclusivity, and change-of-control restrictions.
Open-source components carry separate risks: copyleft licenses may impose open-source obligations on the device's algorithm, and even permissive licenses may require attribution in derivative works. At a minimum, representations and warranties should identify all third-party, open-source, and foundational systems. All necessary assignments and notices regarding change of control should be addressed in advance of closing.
Where diligence identifies a critical open-source or third-party dependency, the next question is whether that dependency can be replaced. If access could be lost and a viable substitute exists, the buyer should seek indemnification for the cost of re-licensing or re-engineering, while weighing the business considerations of any disruption that either path could create. Where substitution is not commercially reasonable or technically feasible, the indemnity should provide a fallback: either a negotiated purchase price adjustment or indemnification based on "loss" definitions that account for the buyer not obtaining the benefit of their bargain.
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These dependencies are not just legal technicalities. They can determine whether a device remains functional after the deal closes. A change-of-control provision in a cloud hosting agreement, for example, could force a costly migration or even shut down the device entirely. Buyers who overlook these details may find themselves owning an asset that cannot operate.
Freedom to operate (FTO) takes on greater importance with this type of technology because there is significant competition and overlapping patents, making inadvertent infringement a real risk. As a practical matter, however, a comprehensive FTO analysis is rarely achievable on a deal timeline. Even where potentially relevant third-party patents can be identified, fully analyzing infringement and invalidity for each would require more time and cost than most transactions can support.
FTO diligence in this space is therefore better framed as a reasonable, targeted review: examining the target's own patent portfolio and any prior clearance work, identifying asserted claims, demand letters, and pending or threatened litigation, and confirming the scope of in-licensed IP.
Representation and warranty insurance (RWI) underwriters generally recognize these limits. Rather than expecting a full FTO analysis, they typically expect to see that reasonable diligence was performed, although coverage may still be limited or excluded for known or specifically identified patent risks. Underwriters may also focus more closely on the target's software development practices, particularly its use and management of open-source components.
Because diligence cannot eliminate FTO risk and insurance may not cover it, deal structure becomes the primary tool for allocating what remains. Where diligence surfaces a specific patent issue, closing can be conditioned on securing an appropriate license or resolving the dispute, or a portion of the purchase price can be held back until the issue is resolved. Where no specific issue has been identified, a holdback for a fixed post-closing period can address the residual risk that a reasonable, but necessarily limited, review leaves behind.
AI-enabled medical devices are changing deal diligence and terms. Training data, third-party dependencies, and patent risk exposure may continue to evolve after the deal closes. Strategic buyers who evaluate these considerations at the outset and address these risks in deal structures and terms will be best positioned to acquire these assets and manage the risks that come with it.
Shamil has a low tolerance for AI that only works in controlled demos. He covers agentic systems and generative AI with the skepticism of someone who has seen too many “revolutionary” tools quietly disappear six months after launch.
