Improving Clinical Decision Support Systems: Explainable AI for Enhanced Disease Prediction in Healthcare

Abstract

AI has changed many industries, including healthcare, by giving doctors and researchers better ways to predict and diagnose diseases. But the lack of transparency in AI models makes it hard for them to be accepted and used in clinical situations. This abstract looks at the idea of Explainable AI (XAI) and how it can be used in healthcare to make AI-based clinical decision support systems easier to understand and more reliable. XAI is the process of making AI models and methods that explain their decisions and predictions in a way that is clear and easy to understand. In healthcare, XAI is very important because it helps doctors understand why AI suggestions are made. This makes it easier to predict diseases. This knowledge builds trust, makes it easier for AI systems and clinicians to work together, and improves the way clinical decisions are made. To make AI models in healthcare easier to understand, XAI methods like rule-based models, feature importance analysis, and model-agnostic techniques have been made. With these methods, healthcare workers can find the most important factors that affect predictions and test the AI system's accuracy and reliability. Putting XAI into clinical decision support tools has a number of advantages. It makes disease predictions more accurate and clearer, speeds up professional workflow, cuts down on mistakes, and, in the end, improves patient outcomes. Also, XAI gives healthcare workers the tools to find and fix biases in AI models. This makes sure that everyone gets fair and equal care. In conclusion, XAI has a lot of potential to improve healthcare disease forecasts by making AI models clearer and easier to understand. By bridging the gap between what AI can predict and what humans can understand, XAI lets clinicians trust AI-driven insights and use them successfully to improve patient care. Future research should focus on standardizing and evaluating XAI methods, as well as solving problems related to privacy, security, and following rules.