  As machine learning models become increasingly prevalent in medical
diagnostics, the need for interpretability and transparency becomes paramount.
The XAI Renaissance signifies a significant shift in the field, aiming to
redefine the interpretability of medical diagnostic models. This paper explores
the innovative approaches and methodologies within the realm of Explainable AI
(XAI) that are revolutionizing the interpretability of medical diagnostic
models. By shedding light on the underlying decision-making process, XAI
techniques empower healthcare professionals to understand, trust, and
effectively utilize these models for accurate and reliable medical diagnoses.
This review highlights the key advancements in XAI for medical diagnostics and
their potential to transform the healthcare landscape, ultimately improving
patient outcomes and fostering trust in AI-driven diagnostic systems.
