<ul data-eligibleForWebStory="true"><li>Researchers introduce xHAIM (Explainable HAIM) as a framework leveraging Generative AI for improved performance and explainability in medical applications.</li><li>xHAIM enhances prediction and explainability by identifying task-relevant patient data, generating patient summaries, improving predictive modeling, and providing clinical explanations.</li><li>Evaluation on the HAIM-MIMIC-MM dataset shows xHAIM boosts average AUC from 79.9% to 90.3% for chest pathology and operative tasks, making AI more transparent and useful for clinicians.</li><li>The xHAIM framework bridges the gap between AI advancements and clinical utility by transforming AI into an explainable decision support system.</li></ul>

Holistic Artificial Intelligence in Medicine; improved performance and explainability

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