Explainable Artificial Intelligence in Precision Medicine: Methods, Applications, and Challenges

Precision medicine focuses on customizing diagnostic, prevention and treatment approaches by accounting for the individual characteristics of each patient. This personalization draws on diverse sources of information including clinical records, genomic data, medical imaging, lifestyle patterns and environmental factors. As the volume and complexity of such multimodal healthcare data continue to expand, machine learning (ML) and deep learning (DL) techniques have become crucial for identifying complex patterns, estimating disease risk, and supporting personalized treatment decisions. Despite their efficiency, many of these models function as opaque systems, generating forecasts without clearly indicating the reasoning behind them. This lack of transparency can undermine clinician confidence, hinder adoption in clinical practice, and raise ethical as well as regulatory concerns, particularly in healthcare contexts where decisions must be explainable and defensible. Explainable Artificial Intelligence (XAI) addresses these challenges by providing methods that make model behaviour more transparent and interpretable. Techniques such as SHAP, LIME, saliency and attention-based visualizations, counterfactual analysis, and rule-based explanations enable clinicians to inspect the rationale behind predictions, evaluate alignment with established medical knowledge, and identify potential sources of bias within data or algorithms. From a patient perspective, explain-ability improves communication, supports informed consent, and strengthens trust in AI-supported care. Regulatory authorities also depend on transparent and interpretable systems to ensure accountability, traceability and compliance with clinical safety requirements. This paper offers a comprehensive examination of explainable AI in the context of precision medicine. It introduces fundamental XAI concepts, organizes key methodological approaches, and reviews applications spanning genomics, medical imaging, and electronic health record (EHR) analytics. The chapter also discusses methods for assessing explanation quality, highlights the role of human-centred design, and addresses critical ethical and legal considerations. It concludes by outlining ongoing challenges and future research directions aimed at developing reliable, interpretable AI systems that can be effectively integrated into advanced personalized healthcare.