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This research aims to develop and validate a multi-modal Explainable AI (XAI) framework for critical healthcare applications.

March 23, 2026Open Access

Explainable Artificial Intelligence for Critical Healthcare Applications

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This research aims to develop and validate a multi-modal Explainable AI (XAI) framework for critical healthcare applications.
Developed ClinXAI integrating Grad-CAM, SHAP, and LIME for interpretable predictions.
Validated across three domains: sepsis prediction, diabetic retinopathy grading, and lung cancer detection.
Employed numerous datasets, including MIMIC-III ICU, EyePACS, and NLST for analysis.
Achieved AUC-ROC scores of 0.943, 0.961, and 0.938 for respective tasks.
Clinician trust rated at 4.31 out of 5.0, a 41.2% improvement over standard outputs.
Identified creatinine and lactate as key predictors for sepsis, aligning with clinical guidelines.

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Multi-modal Explainable AI framework enhances clinician trust and diagnostic accuracy in critical healthcare settings.

Marco A. Ferretti (2024) studied this question.

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