Advancing Trustworthy Clinical Decision Support Through Explainable Artificial Intelligence: A Framework for Transparent, Interpretable, and Ethical Healthcare Analytics
TL;DR
A narrative review of the evolving image of XAI as a tool to create trustworthy clinical decision support systems and how it can be performed based on the concepts of transparency, interpretability and moral decision-making concludes that explainability is a fundamental component to the use of AI in everyday clinical practice.
Abstract
Artificial intelligence (AI) has become an integral component of clinical decision support systems, improving diagnostic accuracy, risk prediction, treatment planning, and healthcare resource management. But, the "black box" nature of many successful machine learning models has brought up concerns around trust and accountability, fairness and regulatory acceptance in the clinical setting. To address these challenges, Explanatory Artificial Intelligence (XAI) has become a promising method that aims to provide the explainability of the results predicted by the models without compromising the performance of the analysis. The aim of this narrative review is to provide an overview of the evolving image of XAI as a tool to create trustworthy clinical decision support systems and how it can be performed based on the concepts of transparency, interpretability and moral decision-making. It provides a summary of the current literature on essential explainability techniques, how they can be applied to different health care-related problems, and how they help increase trust and transparency in health care decision making. It also addresses human-AI collaboration, model validation, bias mitigation, privacy protection and governance frameworks to enable responsible use of AI. The new emerging developments, such as federated learning, multimodal explainable models, causal reasoning, and generative AI, are also discussed to emphasize future opportunities for clinically reliable and scalable intelligent healthcare systems. The review concludes that explainability is no longer a choice of technical attribute but rather a fundamental component to the use of AI in everyday clinical practice. For safe, equitable and trusted clinical decision support to benefit everyone in the health care sector, transparency, accountability and ethical governance will play a pivotal role.