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Explainable AI in Healthcare: A Systematic Review of Methods for Interpretable Machine Learning
0
Zitationen
5
Autoren
2025
Jahr
Abstract
The adoption of artificial intelligence (AI) in healthcare has led to breakthroughs in disease diagnosis, treatment planning, and patient monitoring. However, the black-box nature of many AI models poses a significant barrier to clinical adoption. Explainable AI (XAI) offers a solution by making model decisions transparent and interpretable to healthcare professionals. This systematic review examines the state-of-the-art XAI techniques applied in healthcare, categorizing them by model-specificity (intrinsic vs. post-hoc), data type (images, text, tabular), and clinical application. We analyze 125 peer-reviewed studies published between 2018 and 2024 across key medical domains. Our findings highlight trends, challenges, and future directions, emphasizing the need for user-centric and domain-specific interpretability. This review serves as a comprehensive reference for researchers and clinicians seeking to implement interpretable AI solutions in healthcare.
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