Explainable Artificial Intelligence XAI in Healthcare A Comprehensive Review
Journal: International Journal of Trend in Scientific Research and Development (Vol.9, No. 6)Publication Date: 2025-12-30
Authors : Kamble V. B Halgare N. M Mohammed Aejaz Tumkur;
Page : 275-277
Keywords : Explainable AI; Healthcare; SHAP; LIME; Medical Diagnosis; Transparency; Clinical Decision Support.;
Abstract
Artificial Intelligence AI has become integral to healthcare, enabling disease prediction, medical imaging analysis, and personalized treatment planning. However, the opaque or “black box” nature of many AI models particularly deep learning creates significant challenges for trust, safety, ethics, and regulatory acceptance. Explainable Artificial Intelligence XAI offers solutions by making AI decisions interpretable, transparent, and accountable. This paper presents a comprehensive study of XAI methods, tools, and applications specifically in the healthcare domain. Feature based, concept based, surrogate models, pixel based explanations, and human centric XAI approaches are explored in detail. Common XAI frameworks such as SHAP, LIME, ELI5, IBM AIF360, and the What If Tool are evaluated for their role in clinical interpretability and fairness. Applications of XAI in Parkinson's disease detection, cancer diagnostics, Alzheimer's prediction, and COVID 19 risk assessment are reviewed, along with broader use cases in cardiovascular diagnostics and treatment planning. Key challenges such as interpretability– performance trade offs, data bias, workflow integration, and ethical concerns are also analyzed. The paper concludes that XAI is essential for bridging the gap between AI technology and clinical decision making. Future research directions include human centered explainability, regulatory frameworks, real time EHR integration, and next generation interpretable deep learning architectures. Kamble V. B | Dr. Halgare N. M | Mohammed Aejaz Tumkur "Explainable Artificial Intelligence (XAI) in Healthcare: A Comprehensive Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-9 | Issue-6 , December 2025, URL: https://www.ijtsrd.com/papers/ijtsrd98807.pdf Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/98807/explainable-artificial-intelligence-xai-in-healthcare-a-comprehensive-review/kamble-v-b
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