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Algorithmovigilance, lessons from pharmacovigilance.
Alan Balendran1, Mehdi Benchoufi2, Theodoros Evgeniou3
1Université Paris Cité and Université Sorbonne Paris Nord, Inserm, INRAE, Center for Research in Epidemiology and StatisticS (CRESS), Paris, France. alan.balendran@u-paris.fr.
NPJ Digital Medicine
|October 2, 2024
Summary
Artificial Intelligence (AI) systems in healthcare require robust monitoring. Adapting pharmacovigilance methods can improve AI safety and mitigate risks from post-deployment incidents.
Area of Science:
- Medical Informatics
- Artificial Intelligence
- Public Health
Background:
- Artificial Intelligence (AI) systems are increasingly used in high-risk sectors like healthcare.
- Despite evaluation efforts, AI systems experience post-deployment incidents, posing significant challenges for mitigation.
- Existing frameworks for drug safety, known as pharmacovigilance, offer a precedent for monitoring real-world performance.
Purpose of the Study:
- To explore the adaptation of pharmacovigilance principles for monitoring AI systems in healthcare.
- To enhance the response to adverse effects and risks associated with AI deployment.
- To provide a foundation for improved safety protocols for AI in healthcare and other domains.
Main Methods:
- Conceptual adaptation of pharmacovigilance principles.
- Literature review of AI safety and drug safety monitoring.
- Discussion of potential frameworks for AI system monitoring.
Main Results:
- Pharmacovigilance offers a transferable model for AI system monitoring.
- Key concepts like adverse event detection and risk assessment can be applied to AI.
- Proposes a structured approach to managing AI-related incidents.
Conclusions:
- Adapting pharmacovigilance is a promising strategy for ensuring AI safety in healthcare.
- This approach can lead to more effective identification and mitigation of AI-related risks.
- The proposed framework has implications for AI safety beyond the healthcare sector.
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