Enhancing Transparency in Healthcare Decision-Making: An Explainable AI Approach
Tapan Thakur, Syyada Shumaila Khurshid, Nivedita Sharma · 2025
Advances in computational technologies have enabled health systems to make accurate diagnoses and develop precise treatment plans for each patient. However, this capability coexists with a major problem: the opacity of complex algorithmic systems, commonly referred to as the “black box” problem. This work explores the contribution of interpretability toward increasing trust, accountability, and usability of such systems in clinical decision-making. The main focus of this chapter is to outline the applications of healthcare computing, discuss the pros and cons, and provide an indepth introduction to techniques of interpretability aimed at increasing transparency. The discussion covers model-agnostic approaches, such as LIME and SHAP, along with model-specific techniques developed for deep learning and ensemble models, demonstrating the potential to enhance transparency in healthcare environments. Case studies and empirical evidence highlight how these tools can be integrated into clinical workflows to illuminate algorithmic decisions. Furthermore, the exploration extends to how computational models can inform and alleviate potential ethical issues and minimize possible biases while increasing adoption by health professionals. The focus is on critical standardized measurements for interpretability to ensure systems positively influence patient outcomes. Efforts to link the high computational powers of complex systems to applied transparency emphasize the crucial need for interpretability in healthcare, fostering a more reliable, ethical, and efficient health environment.