Unlocking the stochastic parrot: Epistemic obligation and the decline of biological plausibility in clinical reality

The current reliance on medical Big Data is producing a mostro ibrido (monstrous hybrid) of statistical significance that lacks biological anchoring. This investigation builds upon the framework of epistemic injustice and epistemic obligation from (Herzog and Branford, 2025) to analyze the systemic marginalization of human expertise in computational medicine. This work performs a critical audit of three high-profile studies where algorithmic outputs generated from large-scale databases stand in direct contradiction to national gold standards and established clinical benchmarks. The analysis reveals profound discrepancies, including the disappearance of approximately 50,000 cancer cases per year in a nationwide database and the creation of statistically significant but biologically impossible correlations. These findings demonstrate that: a) the epistemic obligation toward algorithms compels practitioners to ignore clinical common sense in favor of bureaucratized data anomalies, b) medical AI often operates as a stochastic parrot, capable of recombining vast amounts of data into seemingly coherent patterns that possess no actual contact with underlying pathological truths. This investigation denounces the definitive farewell to the proof of causality in favor of a mechanized version of medical truth. It highlights the urgent necessity to reclaim epistemic agency, arguing that biological reality cannot be fully replaced by a database, regardless of its scale. We must prevent the statistical ghost of a database from replacing the living patient in the clinical decision-making process. • AI in medicine risks becoming a stochastic parrot devoid of clinical meaning. • We audit the epistemic obligation to defer to flawed algorithmic outputs. • Cases show a 22% to 77% divergence between AI data and national gold standards. • Digital bureaucratization causes an algorithmic degradation of clinical evidence. • We advocate for a return to biological plausibility in medical informatics.