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 before clinical decision support is produced. The study combines compact convolutional neural network modeling, embedding-based machine learning classifiers, calibration-aware reliability analysis, threshold-sensitive decision control, and multi-level explainability using Grad-CAM, LIME, and SHAP. Experimental evaluation is conducted on the publicly available PneumoniaMNIST benchmark dataset from the MedMNIST collection. The compact CNN achieved strong discrimination performance with ROC-AUC of 0.9666 and pneumonia recall of 0.9974, while the UVIF-guided diagnostic layer supported reliability-aware model selection and threshold optimization under screening-oriented constraints. Calibration analysis further revealed deviations between predicted probabilities and empirical outcomes, emphasizing the importance of reliability-aware evaluation in medical AI systems. The proposed framework demonstrates that integrating prediction, calibration, explainability, and diagnostic decision control within a unified variational framework can support more transparent, interpretable, and clinically meaningful AI-assisted pneumonia screening systems.