Navigating Technology Headwinds - Current State of AI & Regulatory Compliance in Healthcare
.
Why AI in Healthcare Needs Regulation
The current state of AI and healthcare regulation is defined by rapid clinical adoption, a surge of new rules, and a shift from experimental pilots to tightly governed, “trustworthy” systems integrated into existing medical‑device law. AI now underpins image interpretation, triage, risk prediction, and patient self‑management, often influencing diagnosis and treatment decisions that can directly affect patient safety. At the same time, these systems rely on large, often sensitive datasets and complex models that can embed bias, fail unpredictably, or be attacked through cybersecurity vulnerabilities. Regulators treat many healthcare AI tools as medical devices and impose additional AI‑specific safeguards around transparency, lifecycle management, and data governance.
In the United States, AI that performs diagnosis, treatment recommendations, or similar functions is generally regulated as Software as a Medical Device (SaMD) under the Food and Drug Administration (FDA). By mid‑2025, more than 950 AI/ML‑enabled devices had been cleared, with radiology tools dominating the portfolio and most products using the 510(k) pathway. The FDA has moved from ad hoc clearances toward more structured guidance, including AI/ML SaMD lifecycle documents and finalized recommendations that support “predetermined change control plans” so learning systems can be updated without full re‑review each time while still meeting safety and effectiveness requirements. Despite this maturation, post‑market surveillance remains a concern, with relatively few AI devices associated with reported adverse events, prompting calls for stronger real‑world performance monitoring.
Across regions, international bodies such as the International Medical Device Regulators Forum, OECD, and ISO are shaping technical standards, and policy discussions routinely stress transparency, validity, accountability, and human oversight as the core characteristics of acceptable healthcare AI.
AI Regulatory Best Practices in Healthcare
U.S. healthcare organizations need to regulate medical AI within existing device frameworks or to build new regimes that still anchor on these principles, with an explicit goal of trustworthy, human‑centered AI in health systems.
Listed below are some areas of concentration to focus on when building out your regulatory framework concerning the use of AI in healthcare:
Transparency and documentation across the full product lifecycle
Robust risk management (including intended use, continuous learning, and cybersecurity)
External validation, high‑quality and representative data, bias mitigation, and strong privacy protections
