{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T02:59:15Z","timestamp":1790218755838,"version":"4.1.0"},"reference-count":39,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Genet."],"abstract":"<jats:p>Artificial Intelligence (AI) is revolutionizing gene therapy from an empirical and iterative process to a predictive, data-driven and increasingly intelligent therapeutic development approach. This review covers the application of AI extensively throughout the gene therapy development process, from target therapeutic discovery, identification of disease genes, variant interpretation, integration of multi-omics data, optimization of CRISPR, engineering of the vector, prediction of immunogenicity, manufacturing, and clinical development and post-marketing surveillance. The role of machine learning, deep learning, foundation models, generative AI and digital twins for advancing therapeutic design, boosting vector performance, manufacturing optimization, patient stratification and enabling precision medicine is discussed. Lastly, we provide directions for future research and highlight the need for close multidisciplinary cooperation between computational scientists, biologists, clinicians, industry, and regulatory agencies to facilitate the development of safe, effective and personalized genetic medicines responsibly.<\/jats:p>","DOI":"10.3389\/fgene.2026.1940629","type":"journal-article","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T02:06:06Z","timestamp":1790215566000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence for precision gene therapy: emerging technologies, clinical applications, and future directions"],"prefix":"10.3389","volume":"17","author":[{"given":"Govinder","family":"Flora","sequence":"first","affiliation":[{"name":"Division of Research Connect, Lovely Professional University","place":["Phagwara, Punjab, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abhinay","family":"Thakur","sequence":"additional","affiliation":[{"name":"Division of Research Connect, Lovely Professional University","place":["Phagwara, Punjab, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,24]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"101335","DOI":"10.1016\/j.nexres.2026.101335","article-title":"Artificial intelligence and machine learning in hydrogel: revolutionizing design and optimization \u2013 detailed review","volume":"5","author":"Aboti","year":"2026","journal-title":"Next Res."},{"key":"B2","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1016\/j.inffus.2023.03.008","article-title":"A systematic review of trustworthy and explainable artificial intelligence in healthcare: assessment of quality, bias risk, and data fusion","volume":"96","author":"Albahri","year":"2023","journal-title":"Inf. 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