Key points are not available for this paper at this time.
Explainable Artificial Intelligence (XAI) is increasingly recognized as a vital component in the deployment of AI systems within healthcare settings. This abstract synthesizes findings from fifteen research papers investigating the prevalence, detection methods, and implications of XAI in healthcare. The review highlights the growing interest in XAI applications among healthcare professionals, emphasizing the importance of interpretability in medical decision-making. Various detection methods, including rule-based approaches and machine learning interpretability techniques, are explored, illustrating the diversity of strategies employed to enhance AI transparency. Furthermore, the review examines the ethical implications of XAI in healthcare, addressing concerns surrounding accountability, bias mitigation, and patient privacy. By synthesizing findings from multiple studies, this abstract provides insights into the integration of XAI technologies in healthcare, contributing to the ongoing discourse on ensuring transparency, trust, and ethical considerations in AI-driven medical practices.
- et al. (Tue,) studied this question.