Demystifying Pulmonary Diagnostics: A Novel Explainable AI Framework for Transparent Clinical Decision Support

Abstract

Pulmonary conditions remain a leading cause of morbidity worldwide, demanding accurate and interpretable individual results. This study introduces a new resolvable artificial intelligence(XAI) frame designed to enhance transparency and clinical trust in pulmonary diagnostics. Our methodology combines deep literacy-grounded image analysis with post-hoc interpretability ways, including Grad-CAM and SHAP, to punctuate critical regions in caseX-rays and CT reviews associated with complaint patterns. The frame supports real - time clinical decision - making by furnishing not only high-delicacy prognostications but also visual explanations that align with radiologists’ moxie. Also, the system incorporates multimodal data, such as patient history and pulmonary function test results, to upgrade individual perceptively. Confirmation using different pulmonary datasets demonstrates both superior discovery performance and bettered interpretability, easing clinician understanding and acceptance. By bridging the gap between AI prognostications and mortal logic, this approach promises to strengthen individual confidence, reduce crimes, and promote cooperative, patient-centered care in pulmonary drug.