Strengthening Risk Oversight Through an AI Governance Framework in Life Sciences

According to an article from attorneys at Paul Hastings, as pharmaceutical and medical device companies increasingly integrate AI into research, clinical development, and operations, they face a dual challenge: leveraging innovation while maintaining strict compliance with global regulations. The authors note that an effective AI governance framework is crucial for aligning AI initiatives with corporate strategy, regulatory requirements, and patient safety obligations.

Globally, regulatory approaches vary. The EU’s AI Act imposes stringent requirements on high-risk systems, including those used in medical devices, requiring continuous risk management and conformity assessments. In contrast, the US lacks a federal framework; Colorado’s postponed 2026 AI law remains the most comprehensive state initiative. Under President Trump’s 2025 “AI Action Plan,” federal agencies are shifting away from regulatory expansion toward reducing perceived barriers to innovation. Still, key Biden-era guidance from the FDA and Department of Health and Human Services remains in effect, particularly concerning AI in medical devices and healthcare IT.

The authors outline a three-stage governance model: (1) Concept Review and Approval, bringing together cross-functional stakeholders to assess AI use cases; (2) Design and Deploy, ensuring compliance and documentation during model development; and (3) Continuous Monitoring and Validation, maintaining oversight post-deployment. Central to this structure are executive sponsorship, robust documentation, and alignment with OECD and NIST principles emphasizing transparency, traceability, and accountability.

For compliance leaders, building a comprehensive and adaptable AI governance framework is vital for managing regulatory uncertainty and maintaining defensibility.