"Bridging Accuracy AND Transparency: Explainable Ai IN Healthcare -A Review"

Abstract

The application of artificial intelligence (AI) in healthcare has demonstrated revolutionary potential in the areas of patient outcome prediction, treatment planning, and diagnosis. But many high-performing models, particularly deep learning systems, are opaque, which is problematic in high-stakes situations where accountability and interpretability are crucial. This study investigates how Explainable AI (XAI) might improve decision-making in the healthcare industry by fostering greater transparency, trust, and dependability. We explore important XAI approaches, such as interpretable-by-design architectures, model-agnostic methodologies, and visualization techniques, and look at how they are used in clinical decision support systems, electronic health record analysis, and medical imaging. In order to ensure that judgments are in line with clinical and ethical norms, XAI helps close the gap between human expertise and sophisticated computer models by empowering physicians to comprehend the reasoning behind AI-generated recommendations. We also go over issues like treating biases that may result in unfair treatment outcomes, controlling uncertainty, and striking a balance between interpretability and accuracy. In the end, safer and more efficient patient care can result from the improved collaboration between AI systems and healthcare workers, as demonstrated by case studies. Research gaps are identified in the paper's conclusion, including the need for domain-specific interpretability methodologies, uniform evaluation frameworks, and integration strategies that take clinical workflow into consideration. The significance of developing AI systems that are not just clever but also reliable and accountable is highlighted by this work's emphasis on explain ability, opening the door for their responsible implementation in high-stakes healthcare settings.