{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T00:57:14Z","timestamp":1791334634771,"version":"4.3.1"},"reference-count":130,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2020,6,5]],"date-time":"2020-06-05T00:00:00Z","timestamp":1591315200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000062","name":"National Institute of Diabetes and Digestive and Kidney Diseases","doi-asserted-by":"publisher","award":["5F31DK121436-02"],"award-info":[{"award-number":["5F31DK121436-02"]}],"id":[{"id":"10.13039\/100000062","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Type 1 diabetes (T1D) is a chronic health condition resulting from pancreatic beta cell dysfunction and insulin depletion. While automated insulin delivery systems are now available, many people choose to manage insulin delivery manually through insulin pumps or through multiple daily injections. Frequent insulin titrations are needed to adequately manage glucose, however, provider adjustments are typically made every several months. Recent automated decision support systems incorporate artificial intelligence algorithms to deliver personalized recommendations regarding insulin doses and daily behaviors. This paper presents a comprehensive review of computational and artificial intelligence-based decision support systems to manage T1D. Articles were obtained from PubMed, IEEE Xplore, and ScienceDirect databases. No time period restrictions were imposed on the search. After removing off-topic articles and duplicates, 562 articles were left to review. Of those articles, we identified 61 articles for comprehensive review based on algorithm evaluation using real-world human data, in silico trials, or clinical studies. We grouped decision support systems into general categories of (1) those which recommend adjustments to insulin and (2) those which predict and help avoid hypoglycemia. We review the artificial intelligence methods used for each type of decision support system, and discuss the performance and potential applications of these systems.<\/jats:p>","DOI":"10.3390\/s20113214","type":"journal-article","created":{"date-parts":[[2020,6,9]],"date-time":"2020-06-09T05:16:14Z","timestamp":1591679774000},"page":"3214","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":88,"title":["Artificial Intelligence in Decision Support Systems for Type 1 Diabetes"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3293-9680","authenticated-orcid":false,"given":"Nichole S.","family":"Tyler","sequence":"first","affiliation":[{"name":"Artificial Intelligence for Medical Systems Lab, Department of Biomedical Engineering, Oregon Health &amp; Science University, Portland, OR 97239, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter G.","family":"Jacobs","sequence":"additional","affiliation":[{"name":"Artificial Intelligence for Medical Systems Lab, Department of Biomedical Engineering, Oregon Health &amp; Science University, Portland, OR 97239, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1407","DOI":"10.1001\/jama.2016.11708","article-title":"Safety of a Hybrid Closed-Loop Insulin Delivery System in Patients with Type 1 Diabetes","volume":"316","author":"Bergenstal","year":"2016","journal-title":"JAMA"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1707","DOI":"10.1056\/NEJMoa1907863","article-title":"Six-Month Randomized, Multicenter Trial of Closed-Loop Control in Type 1 Diabetes","volume":"381","author":"Brown","year":"2019","journal-title":"New Engl. 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