The precision ceiling: information-theoretic accuracy bounds for responsible AI health screening.

Large language models pass licensing examinations and foundation models match human diagnostic accuracy, yet the sensing modalities that supply these algorithms with data obey unchanged physical limits. This article introduces the precision ceiling: the maximum accuracy attainable by any model operating on a given sensing modality, bounded by the information-theoretic capacity of the measurement channel rather than by algorithmic capacity. Grounded in the data processing inequality, the ceiling is an a priori constraint whose clinical magnitude must be empirically characterized per modality. We develop a three-layer nested constraint taxonomy (Physical Law → Physiological Variability → Modality Gap), demonstrate the phenomenon across four optical and photoelectric sensing applications (postural screening, wearable photoplethysmography, fundus imaging, and dermatological AI), and propose two conceptual design contributions-the IICRC design principles (Identify, Internalize, Communicate, Recognize, Cascade) and the Precision Ceiling Audit, a qualitative pre-deployment characterization template-offered for future empirical validation. The framework does not replace existing responsible-AI initiatives; it identifies where humility and safety are physically inevitable rather than aspirational, providing a principled basis for modality-specific accuracy declarations, intended-use restrictions, and escalation protocols aligned with emerging regulatory expectations.