Improving Medical Predictions with Explainable AI

Though the integration of artificial intelligence (AI) in clinical decision-making has led to rapid improvements in medical diagnosis, its "black-box" nature has much more often led to distrust from clinicians and reluctance from patients. Explainable Artificial Intelligence Transparency in automated medical predictions - with machine learning (ML) becoming prevalent, the field of explainable artificial intelligence brings transparency and accountability to studies conducted with ML. This research is aimed at application of XAI in the diagnosis of Diabetic Retinopathy (DR), one of the major causes of preventable blindness in India, through the use of interpretable ML frameworks to promote a deeper understanding of the clinical picture. Through an overview of the existing explainable methodologies and integration with real datasets, this paper underlines how XAI offers the clinician actionable insights and visual interpretability maps, as well as estimations of accuracy. The study pursues addressing the void between the performance of algorithms and medical transparency, while aligning with the developing Indian healthcare ecosystem digitally infrastructure.