Summary:
Understanding the Role of AI in Health and Life Sciences
AI tools now play a central role in clinical development, diagnostics, patient monitoring, and enterprise operations. How AI systems are defined has direct implications for regulation, IP ownership, liability exposure, and compliance obligations. Health and life sciences companies must be attentive to whether their tools are considered high risk under emerging frameworks and whether their use cases fall within FDA oversight, healthcare privacy rules, or state-level AI statutes.
Regulatory and Legislative Landscape
The U.S. continues to operate without a comprehensive federal AI law. Existing laws, however, already regulate many AI use cases, and states have accelerated adoption of AI-specific statutes, with Colorado, Utah, and California leading in 2024–2026. Federal agencies such as the FTC, DOJ, EEOC, and FDA are increasingly active in using existing authority to police AI-enabled products and claims. Globally, the EU AI Act establishes strict requirements for high-risk AI systems, including medical devices, and creates expanded liability exposure under its revised Product Liability Directive.
FDA Oversight of AI-Enabled Technologies
FDA expectations continue to mature with multiple guidance focused on AI-enabled device software functions, lifecycle management, and predetermined change control plans. Enforcement trends show increased scrutiny of validation, substantiation of AI-related claims, and post-market modifications to algorithms. FDA’s use of internal AI systems, such as ELSA for labeling review, signals a shift toward data-driven regulatory evaluation that companies should anticipate in their submissions and internal audits.
Key Legal Developments and Litigation Risks
AI raises novel questions in IP, including ownership of AI-generated outputs, licensing of training data, and safeguarding of trade secrets in automated or collaborative workflows. Litigation risks are expanding across product liability, employment discrimination, privacy, and deceptive trade practices. Courts are increasingly being asked to evaluate AI systems under traditional product liability frameworks, and new EU rules may introduce presumptions of defect and causation, heightening litigation challenges for companies deploying complex or opaque algorithms.
Assessing AI Risks
A robust risk assessment framework must account for data quality, security, and representativeness; explainability limitations inherent in modern models; and reputational risks associated with transparency failures or algorithmic bias. As AI becomes increasingly complex and less interpretable, clear documentation of model design, testing, and ongoing monitoring is essential, particularly given evolving regulatory expectations and litigation trends.
Risk Mitigation and Governance Strategies
Effective AI governance requires alignment with existing compliance processes, establishment of clear policies on permissible AI uses, and ongoing training to ensure enterprise-wide literacy. Companies should implement formal AI risk assessments before deployment, maintain detailed documentation to support regulatory inquiries, and integrate AI risks into board-level oversight structures. Building an AI risk register and providing regular updates on regulatory changes, incident response, and KPIs help ensure that leadership remains informed and prepared.
Future Outlook
AI regulation in the U.S. will continue to develop through a patchwork of state laws, agency guidance, and sector-specific rules. The EU will move into active enforcement of the EU AI Act, creating operational challenges for global life sciences companies. FDA is expected to continue refining its approach to AI/ML-enabled medical technologies. Proactive preparation—including cross-border regulatory alignment, enhanced internal documentation, and early engagement with regulators—will be critical to navigating the next wave of AI oversight.
CLE Information: This program is presumptively approved for 1.0 CLE credit in California, Connecticut, Illinois, New Jersey, New York, Pennsylvania, Texas, and West Virginia. Applications for CLE credit will be filed in Colorado, Delaware, Florida, Georgia, Ohio, and Virginia. Attendees who are licensed in other jurisdictions will receive a uniform certificate of attendance, but Reed Smith only provides credit for the states listed. Please allow 4-6 weeks after the program to receive a certificate of attendance.
