Holistic Artificial Intelligence in Medicine; improved performance and explainability

• URL: http://arxiv.org/abs/2507.00205v1
• Date: Mon, 30 Jun 2025 19:15:06 GMT
• Title: Holistic Artificial Intelligence in Medicine; improved performance and explainability
• Authors: Periklis Petridis, Georgios Margaritis, Vasiliki Stoumpou, Dimitris Bertsimas,
• Abstract summary: xHAIM (Explainable HAIM) is a novel framework leveraging Generative AI to enhance both prediction and explainability.<n>xHAIM improves average AUC from 79.9% to 90.3% across chest pathology and operative tasks.<n>It transforms AI from a black-box predictor into an explainable decision support system, enabling clinicians to interactively trace predictions back to relevant patient data.
• Score: 4.862319939462255
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the increasing interest in deploying Artificial Intelligence in medicine, we previously introduced HAIM (Holistic AI in Medicine), a framework that fuses multimodal data to solve downstream clinical tasks. However, HAIM uses data in a task-agnostic manner and lacks explainability. To address these limitations, we introduce xHAIM (Explainable HAIM), a novel framework leveraging Generative AI to enhance both prediction and explainability through four structured steps: (1) automatically identifying task-relevant patient data across modalities, (2) generating comprehensive patient summaries, (3) using these summaries for improved predictive modeling, and (4) providing clinical explanations by linking predictions to patient-specific medical knowledge. Evaluated on the HAIM-MIMIC-MM dataset, xHAIM improves average AUC from 79.9% to 90.3% across chest pathology and operative tasks. Importantly, xHAIM transforms AI from a black-box predictor into an explainable decision support system, enabling clinicians to interactively trace predictions back to relevant patient data, bridging AI advancements with clinical utility.

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