AI is Transforming Drug Safety—But Governance Sets Essential Guardrails

AI is accelerating drug safety by rapidly analyzing massive data sets, spotting adverse drug events (ADEs) more efficiently from electronic health records, social media, and other real-world sources2 15 9.

Major AI advances include automated case intake, earlier safety signal detection, and integrated monitoring across diverse data sources—measurably increasing speed and scope compared to manual review9 12 15.

Landmark projects like OnSIDES offer the most comprehensive open databases for adverse drug events, enabling researchers and clinicians to systematically study, predict, and mitigate medication risks1.

AI models can introduce new risks—such as bias, false results ('hallucinations'), and quality inconsistencies—potentially impacting underrepresented groups and overall patient safety if not properly managed3 8.

Health technology safety leaders and regulators, such as ECRI, FDA, and EMA, emphasize that strong governance is crucial to mitigate risks, ensure transparency, validate AI outputs, and safeguard privacy and equity2 3 4 6 14.

Recent regulatory guidance—including the FDA’s Draft AI Regulatory Guidance—outlines risk-based frameworks for AI credibility, requires rigorous evidence for context-specific use, and sets expectations for transparency, validation, and ongoing oversight2 14.

Global regulatory agencies are updating or developing new frameworks to keep pace with AI advances, aiming to balance innovation with public health, legal accountability, and data integrity2 14.

Human expertise remains necessary—AI supports and augments the work of regulatory scientists and safety professionals, but oversight and interpretive judgment are vital to maintaining ethical and safe use11 4.