Explainable and Trustworthy AI for Healthcare 4.0: A Systematic Review
Abstract
The integration of Artificial Intelligence into healthcare systems has revolutionized diagnosis, treatment planning, and patient monitoring. However, the lack of transparency in AI models poses challenges in trust and adoption. This paper presents a systematic review of Explainable AI (XAI) techniques in Healthcare 4.0. It analyzes methods such as SHAP, LIME, and attention-based models for improving interpretability in clinical decision-making. The study evaluates applications in medical imaging, predictive diagnostics, and personalized medicine. Key challenges, including regulatory compliance, ethical concerns, and data bias, are discussed. The paper concludes with future research directions focusing on human-centered AI and robust validation frameworks.
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