The Big Inference Blueprint: A Post-Big Data Epistemology for AI in Healthcare

Intelligent Healthcare Spaces represent the frontier of personalized medicine; while we support the Big Data revolution, its current trajectory is often failing clinical expectations and deviating into empirical noise. The promise of personalized care can only be fulfilled if paired with an equally robust inference framework. The field currently suffers from a Big Data hangover where statistical significance (p-value < 0.05) is weaponized to bypass biological plausibility, allowing mathematical volume to mask clinical irrelevance. By promoting implausible signals to the rank of medical law, future AI healthcare models risk treating negligible statistical fluctuations as definitive medical evidence, thus imposing a clinical narrative that distorts reality. This paper introduces, instead, the concept of Big Inference blueprint devised to restore the self determination of patients through reality-anchored Fisherianism, a statistical framework dedicated to the forensic verification of biological truth rather than the mechanized computation of void significance. By leveraging rigorous inferential statistics and national demographic benchmarking, we challenge the prevailing black-box epistemology of medical AI. Through a forensic audit of three case studies (longevity, psychiatry, and oncology), we demonstrate how neglecting demographic integrity leads to algorithmic determinism. Finally, by exposing the systemic resistance of humans not inthe-loop (by choice), we argue for embedding forensic biostatistical checks into the AI's decision-making procedure to ensure that sensing technologies serve the individuals rather than imposing an unjustified biological determinism.