From black box to clear box: explainable AI for next-gen pharmacovigilance.

Nirmala Suryadevara; Vishnu Priya; Sanjay Sharma

doi:10.1080/14740338.2026.2628822

February 6, 2026

From black box to clear box: explainable AI for next-gen pharmacovigilance.

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Key Points

The research aims to explore the integration of explainable AI in pharmacovigilance to improve transparency and safety signal detection.
Review of EU AI Act and its implications for pharmacovigilance (PV) algorithms.
Analysis of the FDA's Good Machine Learning Practice (GMLP) principles.
Assessment of India's Responsible AI principles and their relevance to PV.
Recommendation for a regulatory-fit explainable AI (XAI) framework.
Identified the need for increased human oversight in AI-driven pharmacovigilance.
Highlighted potential for improved safety signal detection through explainable AI methods.
Proposed convergence of governance and human oversight with XAI for enhanced international trust.

Implication

A review emphasizes regulatory-fit explainable AI frameworks to enhance safety signal detection in pharmacovigilance, suggesting better governance and oversight.

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Cite This Study

Suryadevara et al. (2026) studied this question.

synapsesocial.com/papers/698828990fc35cd7a8848261 https://doi.org/10.1080/14740338.2026.2628822

View Full Paper

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From black box to clear box: explainable AI for next-gen pharmacovigilance.

Key Points

Implication

Discussion

Cite This Study

From black box to clear box: explainable AI for next-gen pharmacovigilance.

Key Points

Implication

Discussion

Cite This Study

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