FDA’s January 2026 “Guiding Principles of Good AI Practice in Drug Development” lays out ten expectations for regulated AI, including human-centric design, a risk-based approach, clear context of use, multidisciplinary expertise, data governance, lifecycle management, and clear essential information. Those principles are not PV-specific, but they fit naturally with pharmacovigilance use cases such as case prioritization, literature surveillance, duplicate detection, and signal support.
For PV teams, the most practical lesson is that AI should not be treated like an isolated tool purchase. FDA’s framework suggests that regulated AI must be understood in terms of what it is allowed to do, who reviews the output, how performance is monitored, and how changes are controlled over time. In pharmacovigilance, that makes governance at least as important as functionality.
That is why the strongest digital PV teams in 2026 are likely to be the ones that combine innovation with procedural discipline. The future may reward not the teams using the most AI, but the teams using it in the most controlled and explainable way. This last point is an inference from FDA’s principles.
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