Explainable AI in Neurological Imaging: Methods Reviewed, Gaps Identified

A recent review of 77 studies on explainable artificial intelligence (XAI) in neurological medical imaging has identified key methods and significant gaps. The paper highlights techniques such as Grad-CAM and SHAP, while also pointing out a lack of thorough clinical validation for many XAI approaches in this domain. AI

IMPACT Highlights critical areas for future research in making AI models in medical imaging more transparent and trustworthy for clinical use.

RANK_REASON The cluster contains a review paper analyzing existing research in a specific AI subfield. [lever_c_demoted from research: ic=1 ai=1.0]

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• explainable AI
• Grad-CAM
• Neurological Medical Imaging
• SHAP

• paper
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