Demystifying Explainable Artificial Intelligence (XAI) in Healthcare

Artificial intelligence has a growing presence in healthcare, which helps in diagnosing, predicting risks, clinical decision-making, and planning of resources. Although they are performing well, most AI systems are hard to understand, posing issues of trust, accountability, and safe clinical application. Explainable Artificial Intelligence (XAI) fills this gap and allows understanding how models produce their results, as well as enabling informed human supervision. The chapter covers the history of AI in healthcare, explains the major concepts of XAI including interpretability, transparency, and global versus local explanation, and provides an effective taxonomy of explainability methods. Its use in medical imaging, clinical decision support, disease risk prediction, precision medicine, and health economics are mentioned. Such ethical, regulatory, and implementation issues are discussed, as well as new directions such as explainability-by-design and human-in-the-loop systems. Combined, these views make XAI a vital part of reliable and accountable healthcare AI.