An AI for diseases or a sick AI? The epistemological pathology of big data overload and the loss of statistical significance

Abstract

Modern clinical epidemiology and artificial intelligence are increasingly driven by an idealized premise: the belief that massive databases and advanced machine learning can redeem causal inference from observational uncertainty. Behind the facade of multi-million-record cohorts, however, a profound epistemological crisis festers, revealing a potentially sick artificial intelligence. Modern analytical systems leverage massive sample sizes to generate ultra-significant asymptotic p-values that reflect mathematical outcomes rather than biological reality. This article diagnoses this systemic pathology, the uncritical application of Gaussian asymptotic statistics to sparse, discrete, and rare medical event counts governed by Poisson distributions. To cure this condition, we introduce a methodological antidote: an inference stress test that repurposes the exact lower boundary of the exact Poisson confidence interval as a formal null benchmark, utilizing an event-anchored standard error. Across three clinical case studies, spanning autoimmune dermatology baselines, expanded matching cohorts, and oncological lifestyle investigations, our stress test provides evidence that nominal asymptotic significance may sometimes fail to clear the structural resistance threshold, revealing the underlying fragility of scale-induced signals. This proposed framework serves as an essential epistemological filter, separating genuine biological discovery from digital outcomes and ensuring that future medical AI systems learn only from structurally validated knowledge.