Explainable AI (XAI) in Healthcare: Interpretable Models for Clinical Decision Support

Abstract

Explainable Artificial Intelligence (XAI) plays a pivotal role in the healthcare sector by enhancing the transparency and interpretability of machine learning models, particularly in the context of clinical decision support systems. This paper explores the significance of interpretable models in healthcare, focusing on their application in aiding clinical decision-making processes. We delve into the challenges and complexities associated with traditional black-box models, emphasizing the need for transparency in understanding model predictions, especially in critical healthcare scenarios. The study investigates various XAI techniques and their integration into clinical workflows, aiming to bridge the gap between advanced machine learning capabilities and the comprehensibility required for medical professionals. Through a comprehensive review of existing literature and case studies, we highlight successful implementations of XAI in healthcare settings, showcasing the potential impact on patient outcomes and healthcare practices. Furthermore, we discuss the ethical considerations surrounding the deployment of XAI in the medical domain and propose guidelines for responsible and accountable use. This paper serves as a valuable resource for researchers, healthcare practitioners, and policymakers interested in leveraging interpretable models to enhance clinical decision support systems and ultimately improve patient care.