Advancing Healthcare with Explainable AI: Bridging the Gap Between Black-Box Models and Clinical Decision-Making
Abstract
The widespread adoption of artificial intelligence (AI) in healthcare has led to concerns regarding the interpretability and trustworthiness of black-box models. This paper reviews explainable AI (XAI) techniques, such as SHAP (Shapley Additive Explanations), LIME (Local Interpretable Model-Agnostic Explanations), and attention mechanisms, to enhance transparency in AI-driven clinical decision-making. The study examines real-world applications in disease diagnosis, drug discovery, and patient outcome prediction while discussing challenges in balancing model accuracy and interpretability. Recommendations for integrating XAI into healthcare workflows to improve clinician trust and patient safety are also provided.
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