Explainable AI in Healthcare: Enhancing Trust, Transparency, and Ethical Compliance in Medical AI Systems

Artificial Intelligence (AI) has permeated the facet of healthcare by improving clinical diagnosis and treatment protocols and optimizing healthcare institution functionalities. However, recent years have seen the huge application of complex, often non-interpretable, opaque machine learning models in high-risk healthcare applications violating the principles of transparency, trustworthiness, and ethical responsibility. Therefore, Explainable AI (XAI) is the most effective approach as it provides interpretability and transparency and has minimal negative impacts on performance. This paper mainly focuses on the background, approaches, and heaps of potential of XAI for application in the healthcare system, especially in improving clinicians’ trust and patients’ understanding, as well as in meeting regulation and ethical requirements for healthcare AI systems. There are several methods currently advancing in the XAI field, for instance, LIME, SHAP, and Grad-CAM, whose roles are elucidated concerning clinical practices with a focus on medical imagery, diagnostics, and decision-making. The paper also dissects other important ethical and legal standards, including GDPR and HIPAA, in relation to the development of transparent and compliant systems. In expounding on some of these frameworks' archetypes, drawing examples, and benchmarking strategies, we neutrally discuss current paradigms’ strengths and weaknesses and deliberate on possible developments. Based on our work, to advocate for scalability, fairness, and health AI accountability, increased effort on enhancing and incorporating explanation, timely integration with EHRs, and utilizing different professions is necessary. Consequently, this study argues that XAI should serve as a foundation for safe and ethical developments of new technologies in the field of Digital Health