TL;DR
This Review highlights AI and ML frameworks for integrating genomic, multi-omics, and EHR data, and discusses how these approaches are reshaping genomics research as well as clinical practice.
This review synthesizes key developments in ML for oncology, covering foundational algorithms alongside emerging approaches, and describes future directions, including federated learning, graph neural networks, longitudinal modeling, and integration of real-world and wearable data to support precision oncology.
Personalized medicine aims to tailor prevention, diagnosis, and treatment strategies to individual patients by leveraging heterogeneous data sources such as electronic health records, medical imaging, genomic profiles, and real-time physiological signals. Although artificial intelligence has demonstrated remarkable predictive performance in this domain, the widespread clinical adoption of such models remains constrained by their black-box nature, limited transparency, and lack of trust among clinicians and patients. Explainable Artificial Intelligence (XAI) has emerged as a critical paradigm to address these limitations by providing interpretable, transparent, and clinically meaningful insights into model behavior and decision logic. This paper presents a comprehensive examination of explainable AI in personalized medicine, focusing on its role in bridging complex patient data with actionable clinical decisions. The study discusses major XAI methodologies, including intrinsic interpretability models and post hoc explanation techniques, and evaluates their applicability across key medical use cases such as disease risk prediction, treatment response modeling, and clinical decision support systems. Furthermore, challenges related to data heterogeneity, model generalization, ethical compliance, and regulatory acceptance are analyzed. By integrating explainability with predictive accuracy, XAI-driven frameworks have the potential to enhance clinical confidence, support evidence-based decision-making, and improve patientcentric outcomes. The paper concludes by outlining future research directions toward scalable, trustworthy, and regulation-compliant explainable AI systems for next-generation personalized healthcare.
A clinically oriented, pipeline-based synthesis of contemporary AI applications in genomic medicine, focusing on factors that determine model robustness and clinical utility, and common sources of failure in real-world genomic AI systems.
Future research focuses on integrating Internet of Medical Things (IoMT) devices, real-time monitoring, and federated learning to enable privacy-preserving collaboration across healthcare institutions.
It is concluded that AI-driven diagnostic systems have achieved genuine, reproducible performance parity with human specialists on narrow, well-defined tasks, while broader clinical deployment remains constrained by validation, generalizability, and algorithmic-bias challenges that the reviewed literature has only begun to resolve.
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