{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,15]],"date-time":"2026-02-15T14:58:35Z","timestamp":1771167515680,"version":"3.50.1"},"reference-count":190,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T00:00:00Z","timestamp":1734652800000},"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. Artif. Intell."],"abstract":"<jats:p>One of the foremost causes of global healthcare burden is cancer of the gastrointestinal tract. The medical records, lab results, radiographs, endoscopic images, tissue samples, and medical histories of patients with gastrointestinal malignancies provide an enormous amount of medical data. There are encouraging signs that the advent of artificial intelligence could enhance the treatment of gastrointestinal issues with this data. Deep learning algorithms can swiftly and effectively analyze unstructured, high-dimensional data, including texts, images, and waveforms, while advanced machine learning approaches could reveal new insights into disease risk factors and phenotypes. In summary, artificial intelligence has the potential to revolutionize various features of gastrointestinal cancer care, such as early detection, diagnosis, therapy, and prognosis. This paper highlights some of the many potential applications of artificial intelligence in this domain. Additionally, we discuss the present state of the discipline and its potential future developments.<\/jats:p>","DOI":"10.3389\/frai.2024.1446693","type":"journal-article","created":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T06:32:25Z","timestamp":1734676345000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Artificial intelligence: clinical applications and future advancement in gastrointestinal cancers"],"prefix":"10.3389","volume":"7","author":[{"given":"Abolfazl","family":"Akbari","sequence":"first","affiliation":[]},{"given":"Maryam","family":"Adabi","sequence":"additional","affiliation":[]},{"given":"Mohsen","family":"Masoodi","sequence":"additional","affiliation":[]},{"given":"Abolfazl","family":"Namazi","sequence":"additional","affiliation":[]},{"given":"Fatemeh","family":"Mansouri","sequence":"additional","affiliation":[]},{"given":"Seidamir Pasha","family":"Tabaeian","sequence":"additional","affiliation":[]},{"given":"Zahra","family":"Shokati Eshkiki","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2024,12,20]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1186\/s12885-023-10587-x","article-title":"Machine learning-based colorectal cancer prediction using global dietary data","volume":"23","author":"Abdul Rahman","year":"2023","journal-title":"BMC Cancer"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"291","DOI":"10.2217\/fon-2020-0516","article-title":"Healthcare utilization and total costs of care among patients with advanced metastatic gastric and esophageal cancer","volume":"17","author":"Abraham","year":"2021","journal-title":"Future Oncol."},{"key":"ref3","doi-asserted-by":"publisher","first-page":"642","DOI":"10.1016\/j.medj.2021.04.006","article-title":"Machine learning in clinical decision making","volume":"2","author":"Adlung","year":"2021","journal-title":"Med."},{"key":"ref4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12876-022-02626-x","article-title":"Establishing machine learning models to predict the early risk of gastric cancer based on lifestyle factors","volume":"23","author":"Afrash","year":"2023","journal-title":"BMC Gastroenterol."},{"key":"ref5","doi-asserted-by":"publisher","first-page":"2039","DOI":"10.4254\/wjh.v13.i12.2039","article-title":"Deep learning in hepatocellular carcinoma: current status and future perspectives","volume":"13","author":"Ahn","year":"2021","journal-title":"World J. 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