Critical appraisal of artificial intelligence for rare-event recognition: principles and pharmacovigilance case studies

• URL: http://arxiv.org/abs/2510.04341v1
• Date: Sun, 05 Oct 2025 20:05:38 GMT
• Title: Critical appraisal of artificial intelligence for rare-event recognition: principles and pharmacovigilance case studies
• Authors: G. Niklas Noren, Eva-Lisa Meldau, Johan Ellenius,
• Abstract summary: High-stakes AI applications target low-prevalence events, where apparent accuracy can conceal limited real-world value.<n>We outline key considerations for critical appraisal of AI in rare-event recognition.<n>We instantiate the framework in pharmacovigilance, drawing on three studies.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Many high-stakes AI applications target low-prevalence events, where apparent accuracy can conceal limited real-world value. Relevant AI models range from expert-defined rules and traditional machine learning to generative LLMs constrained for classification. We outline key considerations for critical appraisal of AI in rare-event recognition, including problem framing and test set design, prevalence-aware statistical evaluation, robustness assessment, and integration into human workflows. In addition, we propose an approach to structured case-level examination (SCLE), to complement statistical performance evaluation, and a comprehensive checklist to guide procurement or development of AI models for rare-event recognition. We instantiate the framework in pharmacovigilance, drawing on three studies: rule-based retrieval of pregnancy-related reports; duplicate detection combining machine learning with probabilistic record linkage; and automated redaction of person names using an LLM. We highlight pitfalls specific to the rare-event setting including optimism from unrealistic class balance and lack of difficult positive controls in test sets - and show how cost-sensitive targets align model performance with operational value. While grounded in pharmacovigilance practice, the principles generalize to domains where positives are scarce and error costs may be asymmetric.

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