AI-Enabled Medical Tech Faces Regulatory and Patent Hurdles

The FDA’s mission is to safeguard patient safety and public health by ensuring that medical technologies are safe, effective, and reliable before and after they reach the market. Its review process includes premarket evaluation, post-market monitoring, and scrutiny of clinical performance, transparency, and risk management.

Patent law, in contrast, is designed to incentivize innovation by granting inventors a temporary monopoly in exchange for public disclosure. Patentable inventions must be novel, non-obvious, sufficiently described, and fall within eligible subject matter. For AI-enabled medical technology, this means developers must navigate two different—but increasingly intertwined—regulatory regimes: one focused on protecting patients, and the other on rewarding creativity.

Traditional FDA frameworks were not built to accommodate machine learning systems that continuously evolve as they ingest new data, Carey notes. Adaptive AI raises questions about how to ensure ongoing transparency, accuracy, and safety after deployment. In response, the FDA issued guidance in 2024 on predetermined change control plans (PCCPs) and total product lifecycle oversight for AI-enabled medical devices.

PCCPs allow developers to outline in advance how software updates and retraining cycles will be validated, reducing the need for repeated regulatory submissions. The guidance also emphasizes transparency in algorithmic decision-making, bias mitigation to ensure equitable performance across diverse populations, and post-market monitoring to verify safety and effectiveness in real-world settings. By clarifying approval pathways, the FDA aims to provide developers with regulatory predictability while maintaining accountability.

Even after clearing FDA requirements, innovators face intellectual property obstacles. Two major issues stand out: inventorship and subject matter eligibility.

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Under U.S. law, only natural persons can be inventors—a standard reaffirmed in Thaler v. Vidal. This poses problems when AI contributes substantively to invention, such as discovering new biomarkers from clinical data. Companies must specifically document the human contributions involved to preserve patent rights.

Under Alice Corp. v. CLS Bank International and Mayo v. Prometheus, algorithms and diagnostic methods risk being deemed unpatentable as abstract ideas or natural laws. To secure protection, applicants must show that their inventions include “something more”—for example, a novel technical implementation or system-level improvement beyond conventional data processing. Many AI-based diagnostics face this hurdle when claims rest on correlations between biological data and disease outcomes, associations AI is particularly adept at detecting.

When it comes to navigating the twin challenges, Carey suggests, developers should engage early with the FDA to align their design with rules on validation, bias mitigating and post-market monitoring. Adaptive models should be designed to be updated efficiently within the parameters of the FDA’s latest guidance.

While compliance and IP departments are often separate kingdoms within organizations, coordinating patent filings with regulatory timelines and ensuring filings highlight human inputs and technical improvements can also reduce the risk of conflicts.

By synchronizing these strategies, innovators can accelerate time to market while strengthening both regulatory compliance and patent protection.