Abstract JOURNAL/jpin/04.03/02275668-990000000-00071/figure1/v/2026-04-14T101804Z/r/image-tiff Precision medicine (PM) represents a paradigm shift in health care – moving from generalized treatment toward individualized care informed by each person’s genetic, environmental, and lifestyle profile. The advent of artificial intelligence (AI) and deep learning (DL) has accelerated this transformation by enabling the integration of vast and heterogeneous biomedical data. This review explores how DL has advanced the analytical power of genomics, radiology, and biomedical imaging, forming the technological core of modern PM. In genomics, AI facilitates variant classification, gene expression modeling, and multiomics data fusion for disease risk prediction. In radiology and medical imaging, convolutional and transformer-based architecture enhance lesion detection, image reconstruction, and radiogenomic mapping. Emerging applications in ophthalmic imaging and digital pathology further demonstrate AI’s role in early diagnosis and personalized therapy planning. Despite remarkable progress, challenges remain in ensuring data interoperability, algorithmic transparency, and ethical governance. The review discusses solutions such as federated learning, explainable AI, and privacy-preserving computation that are essential for trustworthy implementation. Looking ahead, the integration of these technologies aligns with the Society 5.0 vision, fostering a human-centered, sustainable, and equitable healthcare ecosystem where intelligent PM bridges technology, ethics, and global well-being.
Pratap et al. (Tue,) studied this question.