From Black Box to Clinical Trust: A Review of Explainable AI (XAI) Methods in Medical Diagnostics and Clinical Decision Support Systems

The rapid proliferation of deep learning models in clinical settings has been met with a fundamental challenge: their inherent “black box” nature, where high predictive accuracy often comes at the expense of interpretability. This lack of transparency poses significant risks in medical diagnostics, where erroneous decisions can lead to dire consequences and legal liabilities. This paper presents a comprehensive review of the state-of-the-art in Explainable Artificial Intelligence (XAI) within the medical domain. We begin by offering a structured taxonomy of XAI methodologies, distinguishing between post-hoc explanation techniques and intrinsically interpretable models. Subsequently, we delve into the integration of these methods across medical imaging, precision medicine (genomics), and real-time patient monitoring. A critical analysis of interpretability evaluation metrics, focusing on faithfulness, robustness, and human-in-the-loop validation, is presented to address the current lack of standardization. Finally, we discuss emergent challenges, including vulnerability to adversarial attacks and the imperative for regulatory compliance under frameworks like GDPR. Our review concludes that the future of clinical AI lies not solely in maximizing accuracy, but in fostering a synergistic relationship between algorithmic precision and human-understandable reasoning

کلیدواژه ها:

Explainable Artificial Intelligence (XAI) ، Deep Learning ، Medical Imaging ، Clinical Decision Support Systems ، Interpretability ، AI in Healthcare ، Transparency