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 chapter aims to provide a comprehensive overview of how ML and DL can be applied to EHRs for early disease detection and prevention, and highlights methodological advances, practical applications, and two real-world case studies, while addressing the challenges that must be overcome for safe and trustworthy clinic...
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 pre...
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 begu...
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