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Design and development of explainable DL/ML model for medical image analysis with high accuracy and reliability

CORDIS · observation · Publication date unknown

Design and development of explainable DL/ML model for medical image analysis with high accuracy and reliability Despite significant advances in artificial intelligence (AI) for medical imaging, healthcare providers face a critical challenge: current deep learning models function as “black boxes” producing results without offering insight into how these conclusions are reached1. This lack of transparency limits clinical trust, hinders adoption in real-world practice, and ultimately restricts the potential of AI to improve patient care. MED-XAI project focuses on solving a major problem (Explainability and Interpretability) with the analysis of medical images in an AI-based healthcare system . The purpose of MED-XAI is to create an Explainable AI (XAI) model applicable to the diagnosis of Pulmonary Hypertension (PH) based on medical imaging data, making AI results easier for doctors to understand and trust. It combines advanced interpretation methods (SHAP, LIME, Grad-CAM, Bayesian inference) to provide clearer, more reliable ins

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recordType

award

status

SIGNED

region

EU

value

207183.12

unit

EUR