ARTIFICIAL INTELLIGENCE AND EXPLAINABLE AI IN MEDICINE: DISEASE PREDICTION AND EFFECTIVENESS IN CLINICAL DECISION-MAKING
This article examines artificial intelligence (AI), particularly explainable artificial intelligence (XAI), and its application in disease
prediction in medicine. It discusses the operating principles of classification models, explanation methods using SHAP and LIME,
effectiveness in clinical decision-making, and practical limitations associated with XAI outcomes. The paper reviews AI systems
based on electronic health records (EHR) and medical imaging, focusing on their accuracy, sensitivity, and the need for
transparency, as well as methods for enhancing clinical reliability through visualization of model results.
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