Integrating Explainable Artificial Intelligence In Healthcare: Models, Applications, And Challenges

This paper will explore the role of XAI in advancing healthcare systems by examining various explain ability models and techniques, including feature attribution methods, model-agnostic approaches, and interpretable machine learning frameworks. It will highlight key applications of XAI in medical imaging, clinical decision support systems, disease prediction, and personalized medicine, where interpretability will be crucial for ensuring reliability and accountability. Furthermore, the study will discuss emerging challenges such as the trade-off between model accuracy and interpretability, data privacy concerns, lack of standardized evaluation metrics, and integration barriers within real-world clinical settings. Ethical considerations and regulatory requirements will also be analysed to understand the broader implications of deploying XAI in HealthCare. The paper will conclude by emphasizing the need for robust, scalable, and clinically validated XAI solutions that will bridge the gap between complex AI models and human understanding. Future research will focus on developing hybrid models, improving user-centric explanations, and fostering interdisciplinary collaboration to ensure the safe and effective adoption of explainable AI in healthcare.