Explainable Artificial Intelligence (XAI) for Healthcare Diagnostics: Current Landscape, Methodologies, Challenges, and Future Directions

This paper explores the current landscape of Explainable Artificial Intelligence (XAI) in healthcare diagnostics, emphasizing its critical role in enhancing transparency, trust, and interpretability of AI-driven medical decision-making. It investigates various XAI methodologies, highlights the challenges faced in clinical integration, and discusses future directions to bridge the gap between AI model complexity and clinician usability. By synthesizing recent advances and practical applications, this study aims to contribute valuable insights for researchers and practitioners striving to foster responsible and effective AI adoption in healthcare diagnostics.