Analytic Misjudgment of Drug Safety Evidence and Causality: From the Prosecutor’s Fallacy and Simpson’s Paradox to Artificial Intelligence

Drug safety assessment, particularly in the post-marketing setting, is especially vulnerable to analytic misjudgment because it relies on heterogeneous evidence streams, incomplete data, infrequent events, and decisions made under substantial uncertainty. Recurring sources of error include misinterpretation of conditional probabilities, conflation of association with causation, inappropriate denominator and comparator selection, inadequate consideration of background incidence and confounding, aggregation artifacts such as Simpson's paradox, and overinterpretation of exploratory findings arising from multiplicity or repeated testing. Misjudgment may be further amplified by spontaneous reporting data that lack explicit exposure denominators and are susceptible to reporting bias, by fragile or incomplete meta-analyses, and by premature regulatory or public responses to weak or incompletely contextualized signals. Using selected real-world case studies and conceptual examples, this narrative review illustrates how such errors arise and propagate across clinical, regulatory, and public domains, and how they can materially influence causality assessment and decision making. The paper also discusses how artificial intelligence (AI), if implemented without transparency, bias assessment, and clinical oversight, may amplify rather than reduce these vulnerabilities. Greater analytic discipline, clearer communication of uncertainty, triangulation across evidence streams, and careful governance of emerging AI-enabled tools are needed to support more reliable drug safety evaluation.