Explainable AI for Health Care Prediction Bridging Data, Decisions and Clinical Adoptation

Artificial Intelligence (AI) is revolutionizing healthcare through its ability to analyze and process vast amounts of data with high precision. AI models, particularly deep learning, are known for their robust performance in clinical decision-making, patient outcomes, and optimization of healthcare efficiency. AI is transforming all aspects of healthcare, including diagnosis, treatment planning, predictive analytics, and personalized medicine. However, the “black-box” nature of many of these AI models poses a significant challenge, as their lack of transparency and interpretability hinders clinical adoption, which involves not only accuracy but also ethical considerations such as rights and trust. The integration of Explainable Artificial Intelligence (XAI) in healthcare is transforming how medical professionals interpret AI-driven insights, improving trust, transparency, and clinical adoption. This chapter examines the role of XAI in bridging the gap between complex healthcare data and AI-driven decision-making, with real-world clinical implementation. The categorization of different XAI techniques, including transparent models, post-hoc explainability methods, perturbation-based approaches, and gradient-based visualization techniques, highlights their relevance in healthcare. The chapter concludes with final remarks on the challenges and future directions of XAI in clinical adoption using case studies of recent works.