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We also outline key barriers to clinical translation, including lack of standardization, small and geographically skewed training cohorts, domain shift across scanners and institutions, and broader ethical, regulatory, and medicolegal concerns. Finally, we highlight future directions, including federated learning to enable privacy-preserving multicenter model training, and emerging concepts such as virtual and digital twins that may support real-time adaptive therapy. These advances suggest that AI-based prediction of response in rectal cancer could be extremely valuable, but will require methodologically rigorous, multi-institutional efforts to be safely and equitably implemented.<\/jats:p>","DOI":"10.1055\/a-2769-1185","type":"journal-article","created":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T00:25:42Z","timestamp":1770855942000},"page":"200-208","source":"Crossref","is-referenced-by-count":0,"title":["Can Artificial Intelligence Be Used to Predict Response in Rectal Cancer? Current Evidence and Future Possibilities"],"prefix":"10.1055","volume":"39","author":[{"given":"Nicole E.","family":"Lopez","sequence":"additional","affiliation":[{"name":"Division of Colon and Rectal Surgery, Department of Surgery, UC San Diego Health, La Jolla, California, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicholas C.","family":"Neel","sequence":"additional","affiliation":[{"name":"Department of Surgery, UC San Diego Health, La Jolla, California, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"194","published-online":{"date-parts":[[2026,2,11]]},"reference":[{"issue":"03","key":"ref1","first-page":"229","article-title":"Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"74","author":"F Bray","year":"2024","journal-title":"CA Cancer J 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