{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T09:14:36Z","timestamp":1777367676087,"version":"3.51.4"},"reference-count":163,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T00:00:00Z","timestamp":1777075200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Blood glucose level (BGL) prediction, by providing early warnings regarding unsatisfactory glycaemic control and maximising the amount of time BGL remains in the target range, can contribute to minimising both acute and chronic complications related to diabetes. This paper aims to provide an overview of data-driven approaches for BGL prediction in type 1 diabetes mellitus (T1DM). This review summarises different aspects of developing and evaluating data-driven prediction models, including model strategy, model input, prediction horizon, and prediction performance. It also examines applications of recent artificial intelligence (AI) techniques, including deep learning, transfer learning, ensemble learning, and causal analysis in the management of T1DM. Recent studies indicate that machine learning approaches often outperform classical time-series forecasting models in BGL prediction, particularly when using multivariate inputs. These findings also highlight the potential of advanced AI methods to improve prediction accuracy. Moreover, applying appropriate statistical analyses is essential to enable valid comparisons between different BGL prediction models, especially given the considerable inter-individual variability among people with T1DM. The development of efficient methods for integrating affecting variables into BGL prediction requires further research. Given the promising performance of advanced AI techniques and the rapid growth of AI innovation, continued exploration of cutting-edge AI strategies will be crucial for further improving BGL prediction models.<\/jats:p>","DOI":"10.3390\/s26092675","type":"journal-article","created":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T08:12:31Z","timestamp":1777363951000},"page":"2675","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence for Blood Glucose Level Prediction in Type 1 Diabetes: Methods, Evaluation, and Emerging Advances"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6878-875X","authenticated-orcid":false,"given":"Heydar","family":"Khadem","sequence":"first","affiliation":[{"name":"Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield S1 4DE, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3276-3953","authenticated-orcid":false,"given":"Hoda","family":"Nemat","sequence":"additional","affiliation":[{"name":"Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield S1 4DE, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7867-9987","authenticated-orcid":false,"given":"Jackie","family":"Elliott","sequence":"additional","affiliation":[{"name":"Department of Oncology and Metabolism, University of Sheffield, Sheffield S10 2RX, UK"},{"name":"Sheffield Teaching Hospitals, Diabetes and Endocrine Centre, Northern General Hospital, Sheffield S5 7AU, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammed","family":"Benaissa","sequence":"additional","affiliation":[{"name":"Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield S1 4DE, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"S81","DOI":"10.2337\/dc14-S081","article-title":"Diagnosis and classification of diabetes mellitus","volume":"37","author":"Association","year":"2014","journal-title":"Diabetes Care"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.csbj.2016.12.005","article-title":"Machine learning and data mining methods in diabetes research","volume":"15","author":"Kavakiotis","year":"2017","journal-title":"Comput. 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