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.
Marco Roccetti· Frontiers in Artificial Inte...· 0 citations
Paper Accepetd for publication in: Transactions on Artificial Intelligence ISSN: 2982-3439 (Scilight Press) Abstract Researchers increasingly rely on Clinical Artificial Intelligence (CAI) to derive predictive insights from massive observational datasets. However, the paradigm of Big Medical Inference often conflates statistical volume with clinical representativeness. This study argues that when methodological rigor is sacrificed for data scale, AI models risk institutionalizing historical biases, thereby compromising patient safety and public health policy integrity. We propose a precautionary, multi-level auditing framework designed to assess the structural architectural integrity of large-scale clinical datasets prior to computational deployment. Rather than contesting specific clinical outcomes, our approach establishes a quantitative prerequisite for CAI: the validation of demographic representativeness and baseline case distribution against official national benchmarks. To demonstrate the validity of our approach, we applied our framework to a prominent, high-profile case study. Our check revealed a tripartite structural divergence: a 32.5% demographic deficit in high-risk elderly (aged 65 years) strata, a 26.2% aggregate cancer incidence suppression, and a 45.1% deflation of expected cases in non-exposed groups.These discrepancies demonstrate that even massive datasets can be fundamentally misaligned with clinical reality. We conclude that structural validation is not an elective procedure but an ethical imperative for Human-Centric Clinical AI. By re-establishing this hierarchy of validation, we ensure that the intelligence of automated AI systems remains subordinate to the structural truth of the data, thereby transitioning from a reliance on Big Data alone toward a more robust, ethically sound, and authentic practice of clinical knowledge.
Marco Roccetti· Zenodo (CERN European Organi...· 0 citations
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