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| Format: | Recurso digital |
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Zenodo
2025
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| Online Access: | https://doi.org/10.5281/zenodo.17357263 |
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Table of Contents:
- <p>Explainable Artificial Intelligence (XAI) is vital for enabling trust, transparency, and accountability in medical decision support systems. This paper provides a conceptual survey of core XAI techniques— such as LIME, SHAP, Integrated Gradients, Grad-CAM, counterfactual explanations, and concept activation—and analyzes their theoretical foundations, strengths, and limitations in healthcare settings. We further discuss major challenges unique to clinical deployment: explanation validation, human-AI interaction, trust calibration, and regulatory compliance. To guide researchers and practitioners, we propose a conceptual framework mapping XAI methods by clinical task (diagnosis, prognosis, treatment decision), data modality (tabular, imaging, multimodal), and explanation scope (local vs global). We conclude with recommendations for future work: benchmark datasets for explanation evaluation, multimodal interpretation, human‐centered studies, and integration with causal reasoning.</p>