Explainable AI (XAI) in Healthcare: Addressing Trust and Transparency Challenges in Diagnostic Systems

Abstract

This research focuses on the application of Explainable Artificial Intelligence (XAI) techniques to enhance trust and transparency in AI-powered diagnostic systems within healthcare. The study investigates various XAI methodologies (e.g., LIME, SHAP) and their effectiveness in providing intelligible explanations for AI predictions to medical professionals. Through user studies and expert evaluations, the research aims to identify the most effective XAI approaches for different clinical scenarios, ultimately fostering greater adoption and responsible deployment of AI in critical medical decision-making.