Navigating AI Compliance In Life Sciences: FDA Policy Insights For AI Governance

Integrating artificial intelligence into life sciences workflows offers significant operational advantages, yet navigating compliance requires aligning adoption strategies with evolving regulatory frameworks. Regulatory bodies emphasize critical governance pillars for AI tools—including data quality, human oversight, transparent decision-making, continuous monitoring, and robust risk management. Because current AI applications are not fully autonomous, establishing expert human-in-the-loop controls remains essential to verify model outputs, prevent performance drift, and ensure regulatory alignment.

To maintain compliance, organizations should anchor their governance frameworks on established international standards, such as ISO 42001, and prioritize low-risk, assistive use cases over fully automated decision-making. Demanding transparent evidence from software vendors, maintaining tenant-isolated data environments, and forming cross-functional governance committees enable life sciences companies to deploy AI tools safely and defensibly. Watch the full webinar to align your AI adoption strategy with regulatory expectations.