Precision Medicine Using Artificial Intelligence and Genomics: A Comprehensive Review

Precision medicine is an emerging healthcare paradigm that tailors prevention, diagnosis, and treatment to individual patients using genetic, clinical, environmental, and lifestyle information. Advances in next-generation sequencing, bioinformatics, and Artificial Intelligence (AI) now allow clinicians to analyse genomic variants, stratify disease risk, and select therapies at a level of individual detail that population-averaged medicine cannot provide. This paper reviews AI-enabled precision medicine, synthesising landmark and recent peer-reviewed literature published between 2004 and 2026, and tracing the field from early blood-group stratification through the Human Genome Project to national precision medicine programmes and the multimodal AI of the present day. A conceptual framework is proposed that integrates electronic health records, genomic sequencing, laboratory tests, and wearable-device data in a secure platform where AI models identify biomarkers, predict risk, and recommend treatment for clinician validation; it is synthesised from literature-reported practices rather than experimentally validated. Applications in oncology, diabetes, cardiovascular disease, rare genetic disorders, pharmacogenomics, and remote monitoring are examined, followed by the main challenges, including data privacy, algorithmic bias, unrepresentative genomic datasets, cost, interpretability, and ethics, together with mitigation strategies. Emerging directions, including digital twins, federated learning, multimodal AI, and CRISPR-assisted therapies, are discussed. This review highlights the role of AI and genomics in moving healthcare from reactive to predictive and personalised, and identifies priorities for future research.