Diagnostic-Field Variational Intelligence for Trustworthy Pneumonia Screening: A UVIF-Based Framework for Explainable and Calibration-Aware Clinical Decision Support
Artificial intelligence systems for pneumonia detection often achieve strong predictive performance but remain insufficiently calibrated, weakly interpretable, and poorly aligned with clinically meaningful decision-support requirements. This paper presents a diagnostic-field extension of the Unified Variational Intelligence Framework (UVIF) for trustworthy and decision-centric pneumonia screening using chest X-ray imaging. The proposed framework models diagnosis as a variational process in which imaging patterns and latent feature representations are treated as diagnostic fields that must be sensed, filtered, interpreted, and evaluated […]