{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T08:19:35Z","timestamp":1783585175948,"version":"3.55.0"},"reference-count":171,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T00:00:00Z","timestamp":1783555200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T00:00:00Z","timestamp":1783555200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100004504","name":"Lietuvos Mokslo Taryba","doi-asserted-by":"publisher","award":["P-PD-24\u2013169"],"award-info":[{"award-number":["P-PD-24\u2013169"]}],"id":[{"id":"10.13039\/501100004504","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Rev Endocr Metab Disord"],"DOI":"10.1007\/s11154-026-10068-z","type":"journal-article","created":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T06:33:09Z","timestamp":1783578789000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Trustworthy AI for personalized glycemic control: a systematic review and critical appraisal of multimodal forecasting and safety-critical closed-loop control"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1775-2589","authenticated-orcid":false,"given":"Sarmad","family":"Maqsood","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2204-3442","authenticated-orcid":false,"given":"Egle","family":"Belousovien\u0117","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1019-7268","authenticated-orcid":false,"given":"Tomas","family":"Tamosuitis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2809-2213","authenticated-orcid":false,"given":"Rytis","family":"Maskeli\u016bnas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,9]]},"reference":[{"key":"10068_CR1","doi-asserted-by":"crossref","unstructured":"Amer Diabet Assoc Professional Practice\u00a0Comm NA, Elsayed NA, Mccoy RG,\u00a0Aleppo G,\u00a0Balapattabi K, Beverly EA,\u00a0Early B,\u00a0Bruemmer D, Echouffo-Tcheugui JB,\u00a0Ekhlaspour L, et\u00a0al. 6. glycemic goals and hypoglycemia: Standards of care in diabetes-2025. Diabet Care. 2025;48:S128\u2013S145.","DOI":"10.2337\/dc25-S006"},{"issue":"1","key":"10068_CR2","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/S2213-8587(22)00319-9","volume":"11","author":"T Battelino","year":"2023","unstructured":"Battelino T, Alexander CM, Amiel SA, Arreaza-Rubin G, Beck RW, Bergenstal RM, et al. Continuous glucose monitoring and metrics for clinical trials: an international consensus statement. Lancet Diabet Endocrinol. 2023;11(1):42\u201357.","journal-title":"Lancet Diabet Endocrinol"},{"issue":"12","key":"10068_CR3","doi-asserted-by":"publisher","first-page":"1631","DOI":"10.2337\/dc17-1600","volume":"40","author":"T Danne","year":"2017","unstructured":"Danne T, Nimri R, Battelino T, Bergenstal RM, Close KL, DeVries JH, et al. International consensus on use of continuous glucose monitoring. Diabet Care. 2017;40(12):1631\u201340.","journal-title":"Diabet Care"},{"key":"10068_CR4","doi-asserted-by":"crossref","unstructured":"Maqsood S, Sarwar MA,\u00a0Belousoviene E,\u00a0Maskeli\u00fcnas R. Explainable multi-task deep learning for blood glucose forecasting: A lightweight, interpretability focused approach. In: SETSCI-Conference Proceedings, vol.\u00a022.\u00a0\u00a0\u00a0SETSCI-Conference Proceedings. 2025. p. 1\u20136.","DOI":"10.36287\/setsci.22.4.001"},{"issue":"12","key":"10068_CR5","doi-asserted-by":"publisher","first-page":"7416","DOI":"10.1111\/dom.70147","volume":"27","author":"S Maqsood","year":"2025","unstructured":"Maqsood S, Sarwar MA, Belousovien\u0117 E, Maskeli\u016bnas R. Gluconet-mm: A multimodal attention-based multi-task learning framework with decision transformer for personalised and explainable blood glucose forecasting. Diabetes Obes Metab. 2025;27(12):7416\u201330.","journal-title":"Diabetes Obes Metab"},{"issue":"3","key":"10068_CR6","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1056\/NEJMoa2111673","volume":"386","author":"J Ware","year":"2022","unstructured":"Ware J, Allen JM, Boughton CK, Wilinska ME, Hartnell S, Thankamony A, et al. Randomized trial of closed-loop control in very young children with type 1 diabetes. N Engl J Med. 2022;386(3):209\u201319.","journal-title":"N Engl J Med"},{"issue":"10","key":"10068_CR7","doi-asserted-by":"publisher","first-page":"882","DOI":"10.1056\/NEJMoa2203496","volume":"387","author":"CK Boughton","year":"2022","unstructured":"Boughton CK, Allen JM, Ware J, Wilinska ME, Hartnell S, Thankamony A, et al. Closed-loop therapy and preservation of c-peptide secretion in type 1 diabetes. N Engl J Med. 2022;387(10):882\u201393.","journal-title":"N Engl J Med"},{"issue":"10270","key":"10068_CR8","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1016\/S0140-6736(20)32514-9","volume":"397","author":"RM Bergenstal","year":"2021","unstructured":"Bergenstal RM, Nimri R, Beck RW, Criego A, Laffel L, Schatz D, et al. A comparison of two hybrid closed-loop systems in adolescents and young adults with type 1 diabetes (flair): a multicentre, randomised, crossover trial. Lancet. 2021;397(10270):208\u201319.","journal-title":"Lancet"},{"key":"10068_CR9","doi-asserted-by":"crossref","unstructured":"Di Molfetta S, Di Gioia L, Caruso I, Caporusso M, Trerotoli P, Natalicchio A, et al. Glycaemic control and variability with different commercially available hybrid closed loop systems in people with type 1 diabetes: A systematic review and meta-analysis of randomized controlled trials. Diabetes Obes Metab. 2025.","DOI":"10.1177\/29986702261437858"},{"issue":"1","key":"10068_CR10","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1186\/s13054-017-1784-0","volume":"21","author":"JS Krinsley","year":"2017","unstructured":"Krinsley JS, Chase JG, Gunst J, Martensson J, Schultz MJ, Taccone FS, et al. Continuous glucose monitoring in the icu: clinical considerations and consensus. Crit Care. 2017;21(1):197.","journal-title":"Crit Care"},{"key":"10068_CR11","doi-asserted-by":"publisher","first-page":"108504","DOI":"10.1016\/j.clineuro.2024.108504","volume":"245","author":"E Garcia-Ballestas","year":"2024","unstructured":"Garcia-Ballestas E, Villafa\u00f1e J, Nu\u00f1ez-Baez K, Perdomo WAF, Duran MA, Janjua T, et al. A systematic review and meta-analysis on glycemic control in traumatic brain injury. Clin Neurol Neurosurg. 2024;245:108504.","journal-title":"Clin Neurol Neurosurg"},{"key":"10068_CR12","doi-asserted-by":"crossref","unstructured":"Maqsood S,\u00a0Belousoviene E,\u00a0Maskeliunas R. Attention-based multi-task learning and ppo reinforcement learning for explainable blood glucose prediction. 2025.","DOI":"10.15439\/2025F2640"},{"issue":"14","key":"10068_CR13","doi-asserted-by":"publisher","first-page":"3870","DOI":"10.3390\/s20143870","volume":"20","author":"M Vettoretti","year":"2020","unstructured":"Vettoretti M, Cappon G, Facchinetti A, Sparacino G. Advanced diabetes management using artificial intelligence and continuous glucose monitoring sensors. Sensors. 2020;20(14):3870.","journal-title":"Sensors"},{"issue":"6","key":"10068_CR14","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1089\/dia.2018.0364","volume":"21","author":"DP Zaharieva","year":"2019","unstructured":"Zaharieva DP, Turksoy K, McGaugh SM, Pooni R, Vienneau T, Ly T, et al. Lag time remains with newer real-time continuous glucose monitoring technology during aerobic exercise in adults living with type 1 diabetes. Diabet Technol Therapeut. 2019;21(6):313\u201321.","journal-title":"Diabet Technol Therapeut"},{"issue":"9","key":"10068_CR15","doi-asserted-by":"publisher","first-page":"e0310801","DOI":"10.1371\/journal.pone.0310801","volume":"19","author":"S Ghimire","year":"2024","unstructured":"Ghimire S, Celik T, Gerdes M, Omlin CW. Deep learning for blood glucose level prediction: How well do models generalize across different data sets? PLoS ONE. 2024;19(9):e0310801.","journal-title":"PLoS ONE"},{"issue":"8","key":"10068_CR16","doi-asserted-by":"publisher","first-page":"e36010","DOI":"10.2196\/36010","volume":"24","author":"A Ahmed","year":"2022","unstructured":"Ahmed A, Aziz S, Abd-Alrazaq A, Farooq F, Sheikh J. Overview of artificial intelligence-driven wearable devices for diabetes: scoping review. J Med Internet Res. 2022;24(8):e36010.","journal-title":"J Med Internet Res"},{"key":"10068_CR17","unstructured":"Marling C,\u00a0Bunescu R. The ohiot1dm dataset for blood glucose level prediction: Update 2020. In: CEUR workshop proceedings. 2020. vol. 2675. p.\u00a071."},{"issue":"12","key":"10068_CR18","doi-asserted-by":"publisher","first-page":"2093","DOI":"10.3390\/s16122093","volume":"16","author":"A Facchinetti","year":"2016","unstructured":"Facchinetti A. Continuous glucose monitoring sensors: past, present and future algorithmic challenges. Sensors. 2016;16(12):2093.","journal-title":"Sensors"},{"issue":"1","key":"10068_CR19","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1177\/1932296813514502","volume":"8","author":"CD Man","year":"2014","unstructured":"Man CD, Micheletto F, Lv D, Breton M, Kovatchev B, Cobelli C. The uva\/padova type 1 diabetes simulator: new features. J Diabetes Sci Technol. 2014;8(1):26\u201334.","journal-title":"J Diabetes Sci Technol"},{"key":"10068_CR20","doi-asserted-by":"crossref","unstructured":"Visentin R, Dalla Man C, Kovatchev B, Cobelli C. The university of virginia\/padova type 1 diabetes simulator matches the glucose traces of a clinical trial. Diabet Technol Therapeut. 2014;16(7):428\u201334.","DOI":"10.1089\/dia.2013.0377"},{"issue":"1","key":"10068_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41597-022-01899-x","volume":"10","author":"AE Johnson","year":"2023","unstructured":"Johnson AE, Bulgarelli L, Shen L, Gayles A, Shammout A, Horng S, et al. Mimic-iv, a freely accessible electronic health record dataset. Scientif Data. 2023;10(1):1.","journal-title":"Scientif Data"},{"issue":"1","key":"10068_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2018.178","volume":"5","author":"TJ Pollard","year":"2018","unstructured":"Pollard TJ, Johnson AE, Raffa JD, Celi LA, Mark RG, Badawi O. The eicu collaborative research database, a freely available multi-center database for critical care research. Scientif Data. 2018;5(1):1\u201313.","journal-title":"Scientif Data"},{"key":"10068_CR23","unstructured":"Faltys M,\u00a0Zimmermann M,\u00a0Lyu X,\u00a0H\u00fcser M,\u00a0Hyland S,\u00a0R\u00e4tsch G,\u00a0Merz T. Hirid, a high time-resolution icu dataset (version 1.1. 1). Physio Net. 2021;10."},{"issue":"1","key":"10068_CR24","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1093\/jamia\/ocw042","volume":"24","author":"BA Goldstein","year":"2016","unstructured":"Goldstein BA, Navar AM, Pencina MJ, Ioannidis JP. Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review. J Am Med Inf Assoc JAMIA. 2016;24(1):198.","journal-title":"J Am Med Inf Assoc JAMIA"},{"key":"10068_CR25","unstructured":"Johnson AE, Pollard TJ, Mark RG (2017) Reproducibility in critical care: a mortality prediction case study. In: Machine learning for healthcare conference. PMLR; 2017. p. 361\u2013376."},{"issue":"11","key":"10068_CR26","doi-asserted-by":"publisher","first-page":"1716","DOI":"10.1038\/s41591-018-0213-5","volume":"24","author":"M Komorowski","year":"2018","unstructured":"Komorowski M, Celi LA, Badawi O, Gordon AC, Faisal AA. The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care. Nat Med. 2018;24(11):1716\u201320.","journal-title":"Nat Med"},{"issue":"4","key":"10068_CR27","doi-asserted-by":"publisher","first-page":"655","DOI":"10.2337\/dc24-2510","volume":"48","author":"LM Wilson","year":"2025","unstructured":"Wilson LM, Herzig SJ, Marcantonio ER, Steinman MA, Schonberg MA, Wang BX, et al. Management of diabetes and hyperglycemia in the hospital: A systematic review of clinical practice guidelines. Diabetes Care. 2025;48(4):655\u201364.","journal-title":"Diabetes Care"},{"issue":"8","key":"10068_CR28","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9(8):1735\u201380.","journal-title":"Neural Comput"},{"key":"10068_CR29","doi-asserted-by":"crossref","unstructured":"Cho K,\u00a0Van\u00a0Merri\u00ebnboer B,\u00a0Gulcehre C,\u00a0Bahdanau D,\u00a0Bougares F,\u00a0Schwenk H,\u00a0Bengio Y. Learning phrase representations using rnn encoder-decoder for statistical machine translation. 2014. arXiv preprint arXiv:1406.1078","DOI":"10.3115\/v1\/D14-1179"},{"key":"10068_CR30","unstructured":"Bai S. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. 2018. arXiv preprint arXiv:1803.01271"},{"key":"10068_CR31","doi-asserted-by":"crossref","unstructured":"Lea C,\u00a0Vidal R,\u00a0Reiter A, Hager GD. Temporal convolutional networks: A unified approach to action segmentation. In: European conference on computer vision. Springer; 2016. p. 47\u201354.","DOI":"10.1007\/978-3-319-49409-8_7"},{"issue":"4","key":"10068_CR32","doi-asserted-by":"publisher","first-page":"1748","DOI":"10.1016\/j.ijforecast.2021.03.012","volume":"37","author":"B Lim","year":"2021","unstructured":"Lim B, Ar\u0131k S\u00d6, Loeff N, Pfister T. Temporal fusion transformers for interpretable multi-horizon time series forecasting. Int J Forecast. 2021;37(4):1748\u201364.","journal-title":"Int J Forecast"},{"key":"10068_CR33","unstructured":"Vaswani A,\u00a0Shazeer N,\u00a0Parmar N,\u00a0Uszkoreit J,\u00a0Jones L, Gomez AN,\u00a0Kaiser \u0141,\u00a0Polosukhin I. Attention is all you need. Adv Neural Inf Process Syst. 2017;30."},{"key":"10068_CR34","first-page":"6696","volume":"33","author":"P Kidger","year":"2020","unstructured":"Kidger P, Morrill J, Foster J, Lyons T. Neural controlled differential equations for irregular time series. Adv Neural Inf Process Syst. 2020;33:6696\u2013707.","journal-title":"Adv Neural Inf Process Syst"},{"key":"10068_CR35","unstructured":"Rubanova Y, Chen RT, Duvenaud DK. Latent ordinary differential equations for irregularly-sampled time series. Adv Neural Inf Process Syst. 2019;32."},{"key":"10068_CR36","unstructured":"Kipf T. Semi-supervised classification with graph convolutional networks. 2016. arXiv:1609.02907"},{"issue":"1","key":"10068_CR37","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","volume":"32","author":"Z Wu","year":"2020","unstructured":"Wu Z, Pan S, Chen F, Long G, Zhang C, Yu PS. A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Syst. 2020;32(1):4\u201324.","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"10068_CR38","unstructured":"Bronstein MM,\u00a0Bruna J,\u00a0Cohen T,\u00a0Veli\u010dkovi\u0107 P. Geometric deep learning: Grids, groups, graphs, geodesics, and gauges. 2021. arXiv:2104.13478"},{"key":"10068_CR39","unstructured":"Sutton RS, Barto AG, et\u00a0al. Reinforcement learning: An introduction. MIT press Cambridge; 1998. vol.\u00a01. no.\u00a01."},{"key":"10068_CR40","doi-asserted-by":"crossref","unstructured":"Koller T, Berkenkamp F, Turchetta M, Krause A. Learning-based model predictive control for safe exploration. In: 2018 IEEE conference on decision and control (CDC). IEEE; 2018. p. 6059\u201366.","DOI":"10.1109\/CDC.2018.8619572"},{"key":"10068_CR41","doi-asserted-by":"publisher","first-page":"107694","DOI":"10.1016\/j.cmpb.2023.107694","volume":"240","author":"MH Lim","year":"2023","unstructured":"Lim MH, Kim S. A practical approach based on learning-based model predictive control with minimal prior knowledge of patients for artificial pancreas. Comput Methods Programs Biomed. 2023;240:107694.","journal-title":"Comput Methods Programs Biomed"},{"key":"10068_CR42","first-page":"15\u00a0084","volume":"34","author":"L Chen","year":"2021","unstructured":"Chen L, Lu K, Rajeswaran A, Lee K, Grover A, Laskin M, et al. Decision transformer: Reinforcement learning via sequence modeling. Adv Neural Inf Process Syst. 2021;34:15\u00a0084-15\u00a0097.","journal-title":"Adv Neural Inf Process Syst"},{"key":"10068_CR43","unstructured":"Levine S,\u00a0Kumar A,\u00a0Tucker G,\u00a0Fu J. Offline reinforcement learning: Tutorial, review, and perspectives on open problems. 2020. arXiv:2005.01643"},{"key":"10068_CR44","unstructured":"Fujimoto S,\u00a0Meger D,\u00a0Precup D. Off-policy deep reinforcement learning without exploration. In: International conference on machine learning. PMLR; 2019. p. 2052\u20132062."},{"key":"10068_CR45","unstructured":"Yildiz C,\u00a0Heinonen M,\u00a0L\u00e4hdesm\u00e4ki H. Continuous-time model-based reinforcement learning. In: International conference on machine learning. PMLR; 2021, p. 12\u00a0009\u201312\u00a0018."},{"key":"10068_CR46","unstructured":"Guo C,\u00a0Pleiss G,\u00a0Sun Y, Weinberger KQ. On calibration of modern neural networks. In: International conference on machine learning. PMLR; 2017. p. 1321\u20131330."},{"key":"10068_CR47","unstructured":"Kuleshov V,\u00a0Fenner N,\u00a0Ermon S. Accurate uncertainties for deep learning using calibrated regression. In: International conference on machine learning. PMLR; 2018. p. 2796\u20132804."},{"issue":"4","key":"10068_CR48","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1561\/2200000101","volume":"16","author":"AN Angelopoulos","year":"2023","unstructured":"Angelopoulos AN, Bates S, et al. Conformal prediction: A gentle introduction. Foundat Trends\u00ae Mach Learn. 2023;16(4):494\u2013591.","journal-title":"Foundat Trends\u00ae Mach Learn"},{"key":"10068_CR49","unstructured":"Vovk V, Gammerman A, Shafer G. Algorithmic learning in a random world. Springer; 2005."},{"key":"10068_CR50","unstructured":"Romano Y,\u00a0Patterson E,\u00a0Candes E. Conformalized quantile regression. Adv Neural Inf Process Syst. 2019;32."},{"key":"10068_CR51","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.aca.2018.12.009","volume":"1052","author":"C Tronstad","year":"2019","unstructured":"Tronstad C, Elvebakk O, Staal OM, Kalv\u00f8y H, H\u00f8getveit JO, Jenssen TG, et al. Non-invasive prediction of blood glucose trends during hypoglycemia. Anal Chim Acta. 2019;1052:37\u201348.","journal-title":"Anal Chim Acta"},{"key":"10068_CR52","unstructured":"Jin M, Zhang H, Huang D. Deep learning-based early warning model for continuous glucose monitoring data in diabetes management. Spectr Res. 2024;4(1)."},{"issue":"5","key":"10068_CR53","doi-asserted-by":"publisher","first-page":"100182","DOI":"10.1016\/j.slast.2024.100182","volume":"29","author":"X Dong","year":"2024","unstructured":"Dong X, Dong W, Guo X. Diagnosis of acute hyperglycemia based on data-driven prediction models. SLAS Technol. 2024;29(5):100182.","journal-title":"SLAS Technol"},{"issue":"1","key":"10068_CR54","doi-asserted-by":"publisher","first-page":"e47833","DOI":"10.2196\/47833","volume":"11","author":"K Liu","year":"2023","unstructured":"Liu K, Li L, Ma Y, Jiang J, Liu Z, Ye Z, et al. Machine learning models for blood glucose level prediction in patients with diabetes mellitus: systematic review and network meta-analysis. JMIR Med Inform. 2023;11(1):e47833.","journal-title":"JMIR Med Inform"},{"key":"10068_CR55","doi-asserted-by":"crossref","unstructured":"Wilson A,\u00a0Morrison D,\u00a0Sainsbury C,\u00a0Jones G. Narrative review: Continuous glucose monitoring (cgm) in older adults with diabetes. Diabet Therap. 2025;1\u201316.","DOI":"10.1007\/s13300-025-01720-z"},{"key":"10068_CR56","doi-asserted-by":"crossref","unstructured":"Wu X, Zhao X, Chen W, Chen Q, Kong L, Li P. A systematic review of continuous glucose monitoring sensors: principles, core technologies and performance evaluation. Sensors Actuat Rep. 2025;100361.","DOI":"10.1016\/j.snr.2025.100361"},{"key":"10068_CR57","doi-asserted-by":"publisher","first-page":"e51538","DOI":"10.2196\/51538","volume":"26","author":"K Zhang","year":"2024","unstructured":"Zhang K, Huang Q, Wang Q, Li C, Zheng Q, Li Z, et al. Telemedicine in improving glycemic control among children and adolescents with type 1 diabetes mellitus: systematic review and meta-analysis. J Med Internet Res. 2024;26:e51538.","journal-title":"J Med Internet Res"},{"issue":"5","key":"10068_CR58","doi-asserted-by":"publisher","first-page":"87","DOI":"10.3390\/ai6050087","volume":"6","author":"X Yu","year":"2025","unstructured":"Yu X, Yang Z, Sun X, Liu H, Li H, Lu J, et al. Deep reinforcement learning for automated insulin delivery systems: Algorithms, applications, and prospects. AI. 2025;6(5):87.","journal-title":"AI"},{"issue":"7","key":"10068_CR59","doi-asserted-by":"publisher","first-page":"e048008","DOI":"10.1136\/bmjopen-2020-048008","volume":"11","author":"GS Collins","year":"2021","unstructured":"Collins GS, Dhiman P, Navarro CLA, Ma J, Hooft L, Reitsma JB, et al. Protocol for development of a reporting guideline (tripod-ai) and risk of bias tool (probast-ai) for diagnostic and prognostic prediction model studies based on artificial intelligence. BMJ Open. 2021;11(7):e048008.","journal-title":"BMJ Open"},{"key":"10068_CR60","doi-asserted-by":"crossref","unstructured":"Wolff RF, Moons KG, Riley RD, Whiting PF,\u00a0Westwood M, Collins GS, Reitsma JB,\u00a0Kleijnen J,\u00a0Mallett S,\u00a0Group\u2020 P. Probast: a tool to assess the risk of bias and applicability of prediction model studies. Annals Intern Med. 2019;170(1):51\u201358.","DOI":"10.7326\/M18-1376"},{"key":"10068_CR61","doi-asserted-by":"crossref","unstructured":"Whiting PF, Rutjes AW, Westwood ME,\u00a0Mallett S, Deeks JJ, Reitsma JB, Leeflang MM, Sterne JA, Bossuyt PM, Group* Q-. Quadas-2: a revised tool for the quality assessment of diagnostic accuracy studies. Annals Int Med. 2011;155(8):529\u2013536.","DOI":"10.7326\/0003-4819-155-8-201110180-00009"},{"key":"10068_CR62","doi-asserted-by":"crossref","unstructured":"Calzavara A,\u00a0Prendin F,\u00a0Cappon G,\u00a0Del\u00a0Favero S,\u00a0Facchinetti A. Systematic review on deep learning algorithms for blood glucose forecasting in type 1 diabetes. IEEE J Biomed Health Inf. 2026.","DOI":"10.1109\/JBHI.2025.3630214"},{"key":"10068_CR63","doi-asserted-by":"crossref","unstructured":"Page MJ, McKenzie JE, Bossuyt PM,\u00a0Boutron I, Hoffmann TC, Mulrow CD,\u00a0Shamseer L, Tetzlaff JM, Akl EA, Brennan SE, et\u00a0al. The prisma 2020 statement: an updated guideline for reporting systematic reviewsdeclaracion prisma 2020: una guia actualizada para la publicacion de revisiones sistematicas. Revista panamericana de salud publica= Pan Am J Public Health. 2022;46:e112\u2013e112.","DOI":"10.1016\/j.jclinepi.2021.02.003"},{"issue":"1","key":"10068_CR64","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1186\/s13643-020-01542-z","volume":"10","author":"ML Rethlefsen","year":"2021","unstructured":"Rethlefsen ML, Kirtley S, Waffenschmidt S, Ayala AP, Moher D, Page MJ, et al. Prisma-s: an extension to the prisma statement for reporting literature searches in systematic reviews. Syst Rev. 2021;10(1):39.","journal-title":"Syst Rev"},{"key":"10068_CR65","doi-asserted-by":"crossref","unstructured":"Collins GS, Moons KG,\u00a0Dhiman P, Riley RD, Beam AL,\u00a0Van\u00a0Calster B,\u00a0Ghassemi M,\u00a0Liu X, Reitsma JB,\u00a0Van\u00a0Smeden M, et\u00a0al. Tripod+ ai statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ. 2024;385.","DOI":"10.1136\/bmj-2023-078378"},{"issue":"8","key":"10068_CR66","doi-asserted-by":"publisher","first-page":"1593","DOI":"10.2337\/dci19-0028","volume":"42","author":"T Battelino","year":"2019","unstructured":"Battelino T, Danne T, Bergenstal RM, Amiel SA, Beck R, Biester T, et al. Clinical targets for continuous glucose monitoring data interpretation: recommendations from the international consensus on time in range. Diabet Care. 2019;42(8):1593\u2013603.","journal-title":"Diabet Care"},{"key":"10068_CR67","doi-asserted-by":"crossref","unstructured":"ElSayed NA, McCoy RG,\u00a0Aleppo G,\u00a0Bajaj M,\u00a0Balapattabi K, Beverly EA,\u00a0Briggs\u00a0Early K,\u00a0Bruemmer D,\u00a0Cusi K, Echouffo-Tcheugui JB, et\u00a0al. 4. comprehensive medical evaluation and assessment of comorbidities: Standards of care in diabetes\u20142025. Diabet Care. 2025;48.","DOI":"10.2337\/dc25-S004"},{"issue":"5","key":"10068_CR68","doi-asserted-by":"publisher","first-page":"776","DOI":"10.1089\/dia.2005.7.776","volume":"7","author":"WL Clarke","year":"2005","unstructured":"Clarke WL. The original clarke error grid analysis (ega). Diabet Technol Therapeut. 2005;7(5):776\u20139.","journal-title":"Diabet Technol Therapeut"},{"issue":"8","key":"10068_CR69","doi-asserted-by":"publisher","first-page":"1143","DOI":"10.2337\/diacare.23.8.1143","volume":"23","author":"JL Parkes","year":"2000","unstructured":"Parkes JL, Slatin SL, Pardo S, Ginsberg BH. A new consensus error grid to evaluate the clinical significance of inaccuracies in the measurement of blood glucose. Diabet Care. 2000;23(8):1143\u20138.","journal-title":"Diabet Care"},{"issue":"4","key":"10068_CR70","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1177\/1932296814539590","volume":"8","author":"BP Kovatchev","year":"2014","unstructured":"Kovatchev BP, Wakeman CA, Breton MD, Kost GJ, Louie RF, Tran NK, et al. Computing the surveillance error grid analysis: procedure and examples. J Diabet Sci Technol. 2014;8(4):673\u201384.","journal-title":"J Diabet Sci Technol"},{"issue":"3","key":"10068_CR71","doi-asserted-by":"publisher","first-page":"e0118432","DOI":"10.1371\/journal.pone.0118432","volume":"10","author":"T Saito","year":"2015","unstructured":"Saito T, Rehmsmeier M. The precision-recall plot is more informative than the roc plot when evaluating binary classifiers on imbalanced datasets. PLoS ONE. 2015;10(3):e0118432.","journal-title":"PLoS ONE"},{"key":"10068_CR72","unstructured":"Uehara M,\u00a0Shi C,\u00a0Kallus N. A review of off-policy evaluation in reinforcement learning. 2022. arXiv:2212.06355"},{"issue":"8","key":"10068_CR73","first-page":"6939","volume":"35","author":"Y Chandak","year":"2021","unstructured":"Chandak Y, Shankar S, Thomas PS. High-confidence off-policy (or counterfactual) variance estimation. Proceed AAAI Conf Artif Intell. 2021;35(8):6939\u201347.","journal-title":"Proceed AAAI Conf Artif Intell"},{"key":"10068_CR74","unstructured":"Kuleshov V,\u00a0Precup D. Algorithms for multi-armed bandit problems. 2014. arXiv:1402.6028"},{"issue":"21","key":"10068_CR75","doi-asserted-by":"publisher","first-page":"3153","DOI":"10.1002\/sim.1262","volume":"21","author":"K Sidik","year":"2002","unstructured":"Sidik K, Jonkman JN. A simple confidence interval for meta-analysis. Stat Med. 2002;21(21):3153\u20139.","journal-title":"Stat Med"},{"key":"10068_CR76","doi-asserted-by":"crossref","unstructured":"Deeks JJ, Higgins JP, Altman DG, Group CSM. Analysing data and undertaking meta-analyses. Cochr Handbook Systemat Rev Intervent. 2019;241\u2013284.","DOI":"10.1002\/9781119536604.ch10"},{"issue":"3","key":"10068_CR77","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1016\/0197-2456(86)90046-2","volume":"7","author":"R DerSimonian","year":"1986","unstructured":"DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986;7(3):177\u201388.","journal-title":"Control Clin Trials"},{"issue":"1","key":"10068_CR78","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1002\/jrsm.5","volume":"1","author":"LV Hedges","year":"2010","unstructured":"Hedges LV, Tipton E, Johnson MC. Robust variance estimation in meta-regression with dependent effect size estimates. Res Synthes Meth. 2010;1(1):39\u201365.","journal-title":"Res Synthes Meth"},{"key":"10068_CR79","doi-asserted-by":"crossref","unstructured":"Visentin R, Campos-N\u00e1\u00f1ez E, Schiavon M, Lv D, Vettoretti M, Breton M, Kovatchev BP,\u00a0Dalla\u00a0Man C,\u00a0Cobelli C. The uva\/padova type 1 diabetes simulator goes from single meal to single day\u201d. J Diabetes Sci Technol. 2018;12(2):273\u201381.","DOI":"10.1177\/1932296818757747"},{"key":"10068_CR80","doi-asserted-by":"publisher","first-page":"108438","DOI":"10.1016\/j.cmpb.2024.108438","volume":"257","author":"R Giancotti","year":"2024","unstructured":"Giancotti R, Bosoni P, Vizza P, Tradigo G, Gnasso A, Guzzi PH, et al. Forecasting glucose values for patients with type 1 diabetes using heart rate data. Comput Methods Programs Biomed. 2024;257:108438.","journal-title":"Comput Methods Programs Biomed"},{"key":"10068_CR81","doi-asserted-by":"publisher","first-page":"106424","DOI":"10.1016\/j.cmpb.2021.106424","volume":"211","author":"W Seo","year":"2021","unstructured":"Seo W, Park S-W, Kim N, Jin S-M, Park S-M. A personalized blood glucose level prediction model with a fine-tuning strategy: A proof-of-concept study. Comput Methods Programs Biomed. 2021;211:106424.","journal-title":"Comput Methods Programs Biomed"},{"issue":"3","key":"10068_CR82","doi-asserted-by":"publisher","first-page":"308","DOI":"10.1007\/s41666-020-00068-2","volume":"4","author":"T Zhu","year":"2020","unstructured":"Zhu T, Li K, Chen J, Herrero P, Georgiou P. Dilated recurrent neural networks for glucose forecasting in type 1 diabetes. J Healthcare Inf Res. 2020;4(3):308\u201324.","journal-title":"J Healthcare Inf Res"},{"issue":"2","key":"10068_CR83","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1109\/JBHI.2019.2931842","volume":"24","author":"K Li","year":"2019","unstructured":"Li K, Liu C, Zhu T, Herrero P, Georgiou P. Glunet: A deep learning framework for accurate glucose forecasting. IEEE J Biomed Health Inform. 2019;24(2):414\u201323.","journal-title":"IEEE J Biomed Health Inform"},{"issue":"12","key":"10068_CR84","doi-asserted-by":"publisher","first-page":"e0339360","DOI":"10.1371\/journal.pone.0339360","volume":"20","author":"J Xue","year":"2025","unstructured":"Xue J, Cao H, Yang L, Liu J, Zhang Y, Zhang C, et al. Intelligent glucose management in hospitalized patients: Short-term glucose and adverse events prediction. PLoS ONE. 2025;20(12):e0339360.","journal-title":"PLoS ONE"},{"key":"10068_CR85","doi-asserted-by":"publisher","first-page":"102923","DOI":"10.1016\/j.bspc.2021.102923","volume":"69","author":"M Zhang","year":"2021","unstructured":"Zhang M, Flores KB, Tran HT. Deep learning and regression approaches to forecasting blood glucose levels for type 1 diabetes. Biomed Signal Process Control. 2021;69:102923.","journal-title":"Biomed Signal Process Control"},{"issue":"5","key":"10068_CR86","doi-asserted-by":"publisher","first-page":"3123","DOI":"10.1109\/JBHI.2023.3348334","volume":"28","author":"G Annuzzi","year":"2023","unstructured":"Annuzzi G, Apicella A, Arpaia P, Bozzetto L, Criscuolo S, De Benedetto E, et al. Exploring nutritional influence on blood glucose forecasting for type 1 diabetes using explainable ai. IEEE J Biomed Health Inform. 2023;28(5):3123\u201333.","journal-title":"IEEE J Biomed Health Inform"},{"issue":"4","key":"10068_CR87","doi-asserted-by":"publisher","first-page":"487","DOI":"10.3390\/bioengineering10040487","volume":"10","author":"H Khadem","year":"2023","unstructured":"Khadem H, Nemat H, Elliott J, Benaissa M. Blood glucose level time series forecasting: Nested deep ensemble learning lag fusion. Bioengineering. 2023;10(4):487.","journal-title":"Bioengineering"},{"key":"10068_CR88","doi-asserted-by":"publisher","first-page":"1296904","DOI":"10.3389\/fped.2023.1296904","volume":"11","author":"A Marx","year":"2023","unstructured":"Marx A, Di Stefano F, Leutheuser H, Chin-Cheong K, Pfister M, Burckhardt M-A, et al. Blood glucose forecasting from temporal and static information in children with t1d. Front Pediatr. 2023;11:1296904.","journal-title":"Front Pediatr"},{"key":"10068_CR89","doi-asserted-by":"crossref","unstructured":"Shen Y,\u00a0Kleinberg S. Personalized blood glucose forecasting from limited cgm data using incrementally retrained lstm. IEEE Trans Biomed Eng. 2024.","DOI":"10.1109\/TBME.2024.3494732"},{"key":"10068_CR90","doi-asserted-by":"publisher","first-page":"106134","DOI":"10.1016\/j.knosys.2020.106134","volume":"203","author":"F D\u2019Antoni","year":"2020","unstructured":"D\u2019Antoni F, Merone M, Piemonte V, Iannello G, Soda P. Auto-regressive time delayed jump neural network for blood glucose levels forecasting. Knowl-Based Syst. 2020;203:106134.","journal-title":"Knowl-Based Syst"},{"key":"10068_CR91","doi-asserted-by":"publisher","first-page":"127476","DOI":"10.1016\/j.eswa.2025.127476","volume":"280","author":"B Zhu","year":"2025","unstructured":"Zhu B, Connor C. Sugarnet: Personalized blood glucose forecast with a fourier kolmogorov-arnold network. Expert Syst Appl. 2025;280:127476.","journal-title":"Expert Syst Appl"},{"issue":"6","key":"10068_CR92","doi-asserted-by":"publisher","first-page":"2758","DOI":"10.1109\/JBHI.2022.3144870","volume":"26","author":"H Nemat","year":"2022","unstructured":"Nemat H, Khadem H, Eissa MR, Elliott J, Benaissa M. Blood glucose level prediction: advanced deep-ensemble learning approach. IEEE J Biomed Health Inform. 2022;26(6):2758\u201369.","journal-title":"IEEE J Biomed Health Inform"},{"key":"10068_CR93","doi-asserted-by":"crossref","unstructured":"Farahmand E, Azghan RR, Chatrudi NT,\u00a0Kim E, Gudur GK,\u00a0Thomaz E,\u00a0Pedrielli G,\u00a0Turaga P,\u00a0Ghasemzadeh H. Attengluco: Multimodal transformer-based blood glucose forecasting on ai-readi dataset. 2025. arXiv:2502.09919","DOI":"10.1109\/EMBC58623.2025.11251776"},{"key":"10068_CR94","doi-asserted-by":"crossref","unstructured":"Han Y, Kim D-Y, Woo J, Kim J. Glu-ensemble: An ensemble deep learning framework for blood glucose forecasting in type 2 diabetes patients. Heliyon. 2024;10(8).","DOI":"10.1016\/j.heliyon.2024.e29030"},{"issue":"1","key":"10068_CR95","doi-asserted-by":"publisher","first-page":"e13393","DOI":"10.1111\/exsy.13393","volume":"42","author":"M Benaida","year":"2025","unstructured":"Benaida M, Abnane I, Idri A. Deep learning based one step and multi-steps ahead forecasting blood glucose level. Expert Syst. 2025;42(1):e13393.","journal-title":"Expert Syst"},{"issue":"1","key":"10068_CR96","first-page":"6611091","volume":"2021","author":"M Asad","year":"2021","unstructured":"Asad M, Qamar U, Abbas M. Blood glucose level prediction of diabetic type 1 patients using nonlinear autoregressive neural networks. J Healthcare Eng. 2021;2021(1):6611091.","journal-title":"J Healthcare Eng"},{"issue":"23","key":"10068_CR97","doi-asserted-by":"publisher","first-page":"7670","DOI":"10.3390\/s24237670","volume":"24","author":"RI Alkanhel","year":"2024","unstructured":"Alkanhel RI, Saleh H, Elaraby A, Alharbi S, Elmannai H, Alaklabi S, et al. Hybrid cnn-gru model for real-time blood glucose forecasting: Enhancing iot-based diabetes management with ai. Sensors. 2024;24(23):7670.","journal-title":"Sensors"},{"key":"10068_CR98","unstructured":"Farahmand E, Soumma SB, Chatrudi NT,\u00a0Ghasemzadeh H. Hybrid attention model using feature decomposition and knowledge distillation for glucose forecasting. 2024. arXiv:2411.10703"},{"key":"10068_CR99","doi-asserted-by":"crossref","unstructured":"Armandpour M,\u00a0Kidd B,\u00a0Du Y, Huang JZ. Deep personalized glucose level forecasting using attention-based recurrent neural networks. In: 2021 International Joint Conference on Neural Networks (IJCNN). IEEE; 2021. p. 1\u20138.","DOI":"10.1109\/IJCNN52387.2021.9533897"},{"issue":"1","key":"10068_CR100","doi-asserted-by":"publisher","first-page":"24332","DOI":"10.1038\/s41598-021-03341-5","volume":"11","author":"SMA Zaidi","year":"2021","unstructured":"Zaidi SMA, Chandola V, Ibrahim M, Romanski B, Mastrandrea LD, Singh T. Multi-step ahead predictive model for blood glucose concentrations of type-1 diabetic patients. Sci Rep. 2021;11(1):24332.","journal-title":"Sci Rep"},{"issue":"1","key":"10068_CR101","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1186\/s12911-021-01462-5","volume":"21","author":"MF Rabby","year":"2021","unstructured":"Rabby MF, Tu Y, Hossen MI, Lee I, Maida AS, Hei X. Stacked lstm based deep recurrent neural network with kalman smoothing for blood glucose prediction. BMC Med Inform Decis Mak. 2021;21(1):101.","journal-title":"BMC Med Inform Decis Mak"},{"issue":"1","key":"10068_CR102","doi-asserted-by":"publisher","first-page":"13032","DOI":"10.1038\/s41598-025-97391-8","volume":"15","author":"JS Ryu","year":"2025","unstructured":"Ryu JS, Ru JH, Kang Y, Yang S. A deep learning approach for blood glucose monitoring and hypoglycemia prediction in glycogen storage disease. Sci Rep. 2025;15(1):13032.","journal-title":"Sci Rep"},{"key":"10068_CR103","doi-asserted-by":"crossref","unstructured":"Nizam Ozogur H, Ozogur G, Orman Z. Blood glucose level prediction for diabetes based on modified fuzzy time series and particle swarm optimization. Comput Intell. 2021;37(1):155\u201375.","DOI":"10.1111\/coin.12396"},{"key":"10068_CR104","doi-asserted-by":"publisher","first-page":"1936","DOI":"10.1109\/ACCESS.2021.3139578","volume":"10","author":"I Aljamaan","year":"2021","unstructured":"Aljamaan I, Al-Naib I. Prediction of blood glucose level using nonlinear system identification approach. IEEE Access. 2021;10:1936\u201345.","journal-title":"IEEE Access"},{"key":"10068_CR105","doi-asserted-by":"crossref","unstructured":"Alfian G,\u00a0Syafrudin M,\u00a0Rhee J,\u00a0Anshari M,\u00a0Mustakim M,\u00a0Fahrurrozi I. Blood glucose prediction model for type 1 diabetes based on extreme gradient boosting. In: IOP Conference series: Materials science and engineering. IOP Publishing; 2020. vol. 803, no.\u00a01. p. 012012.","DOI":"10.1088\/1757-899X\/803\/1\/012012"},{"issue":"10","key":"10068_CR106","doi-asserted-by":"publisher","first-page":"9333","DOI":"10.1007\/s13369-020-05263-2","volume":"46","author":"A \u015eahin","year":"2021","unstructured":"\u015eahin A, Ayd\u0131n A. Personalized advanced time blood glucose level prediction. Arab J Sci Eng. 2021;46(10):9333\u201344.","journal-title":"Arab J Sci Eng"},{"issue":"1","key":"10068_CR107","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1007\/s12046-024-02657-y","volume":"50","author":"S Arora","year":"2025","unstructured":"Arora S, Kumar S, Kumar P. Forecasting blood glucose level using convolutional recurrent connection. S\u0101dhan\u0101. 2025;50(1):16.","journal-title":"S\u0101dhan\u0101"},{"issue":"6","key":"10068_CR108","doi-asserted-by":"publisher","first-page":"1590","DOI":"10.1177\/19322968221092785","volume":"17","author":"M Jaloli","year":"2023","unstructured":"Jaloli M, Cescon M. Long-term prediction of blood glucose levels in type 1 diabetes using a cnn-lstm-based deep neural network. J Diabetes Sci Technol. 2023;17(6):1590\u2013601.","journal-title":"J Diabetes Sci Technol"},{"key":"10068_CR109","doi-asserted-by":"crossref","unstructured":"Liu VB, Sue LY, Padilla OM,\u00a0Wu Y. Optimizing blood glucose predictions in type 1 diabetes patients using a stacking ensemble approach. Endocr Metabol Sci. 2025;100253.","DOI":"10.1016\/j.endmts.2025.100253"},{"issue":"21","key":"10068_CR110","doi-asserted-by":"publisher","first-page":"7090","DOI":"10.3390\/s21217090","volume":"21","author":"F Tena","year":"2021","unstructured":"Tena F, Garnica O, Lanchares J, Hidalgo JI. Ensemble models of cutting-edge deep neural networks for blood glucose prediction in patients with diabetes. Sensors. 2021;21(21):7090.","journal-title":"Sensors"},{"issue":"5","key":"10068_CR111","doi-asserted-by":"publisher","first-page":"3706","DOI":"10.1109\/JIOT.2022.3143375","volume":"10","author":"T Zhu","year":"2022","unstructured":"Zhu T, Kuang L, Daniels J, Herrero P, Li K, Georgiou P. Iomt-enabled real-time blood glucose prediction with deep learning and edge computing. IEEE Internet Things J. 2022;10(5):3706\u201319.","journal-title":"IEEE Internet Things J"},{"issue":"5","key":"10068_CR112","doi-asserted-by":"publisher","first-page":"1647","DOI":"10.3390\/s21051647","volume":"21","author":"F Prendin","year":"2021","unstructured":"Prendin F, Del Favero S, Vettoretti M, Sparacino G, Facchinetti A. Forecasting of glucose levels and hypoglycemic events: head-to-head comparison of linear and nonlinear data-driven algorithms based on continuous glucose monitoring data only. Sensors. 2021;21(5):1647.","journal-title":"Sensors"},{"issue":"3","key":"10068_CR113","doi-asserted-by":"publisher","first-page":"340","DOI":"10.3390\/diagnostics13030340","volume":"13","author":"H Butt","year":"2023","unstructured":"Butt H, Khosa I, Iftikhar MA. Feature transformation for efficient blood glucose prediction in type 1 diabetes mellitus patients. Diagnostics. 2023;13(3):340.","journal-title":"Diagnostics"},{"issue":"1","key":"10068_CR114","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1109\/TBME.2022.3187703","volume":"70","author":"T Zhu","year":"2022","unstructured":"Zhu T, Li K, Herrero P, Georgiou P. Personalized blood glucose prediction for type 1 diabetes using evidential deep learning and meta-learning. IEEE Trans Biomed Eng. 2022;70(1):193\u2013204.","journal-title":"IEEE Trans Biomed Eng"},{"key":"10068_CR115","unstructured":"Farahmand E, Azghan RR, Chatrudi NT, Ansu-Baidoo VY,\u00a0Kim E, Gudur GK,\u00a0Malu M,\u00a0Krueger O,\u00a0Thomaz E,\u00a0Pedrielli G, et\u00a0al. Glumind: Multimodal parallel attention and knowledge retention for robust cross-population blood glucose forecasting. 2025. arXiv:2509.18457"},{"key":"10068_CR116","doi-asserted-by":"crossref","unstructured":"Fitzgerald O,\u00a0Perez-Concha O,\u00a0Gallego-Luxan B,\u00a0Metke-Jimenez A,\u00a0Rudd L,\u00a0Jorm L. Continuous time recurrent neural networks: overview and application to forecasting blood glucose in the intensive care unit. 2023. arXiv:2304.07025","DOI":"10.1016\/j.jbi.2023.104498"},{"key":"10068_CR117","doi-asserted-by":"crossref","unstructured":"Chen Y,\u00a0Wang Z,\u00a0Tang J,\u00a0Lin H,\u00a0Wu S,\u00a0Huang Y. Directional representation encoder-decoder for personalized blood glucose forecasting. In: Pacific-Asia conference on knowledge discovery and data mining. Springer; 2025. p. 203\u2013215.","DOI":"10.1007\/978-981-96-8197-6_15"},{"key":"10068_CR118","doi-asserted-by":"crossref","unstructured":"Kandemir T,\u00a0Knauer M,\u00a0Coskuny\u00fcrek E,\u00a0Enzweiler M. Predicting blood glucose levels with advanced time series models. In: Computation and communication for smart systems symposium. Springer; 2025. p. 1\u201312.","DOI":"10.1007\/978-3-032-10569-1_1"},{"key":"10068_CR119","doi-asserted-by":"publisher","first-page":"108536","DOI":"10.1016\/j.bspc.2025.108536","volume":"112","author":"M Benaida","year":"2026","unstructured":"Benaida M, Abnane I, Idri A. Stacked ensembles for one-step and multi-step ahead bgl forecasting. Biomed Signal Process Control. 2026;112:108536.","journal-title":"Biomed Signal Process Control"},{"key":"10068_CR120","doi-asserted-by":"crossref","unstructured":"Kalita D, Mirza KB. Multivariate glucose forecasting using deep multihead attention layers inside neural basis expansion networks. IEEE J Biomed Health Inf. 2025.","DOI":"10.1109\/JBHI.2025.3530461"},{"issue":"1","key":"10068_CR121","doi-asserted-by":"publisher","first-page":"30636","DOI":"10.1038\/s41598-025-13491-5","volume":"15","author":"K Moon","year":"2025","unstructured":"Moon K, Kim J, Yoo S, Cho J. Personalized blood glucose prediction in type 1 diabetes using meta-learning with bidirectional long short term memory-transformer hybrid model. Sci Rep. 2025;15(1):30636.","journal-title":"Sci Rep"},{"issue":"6","key":"10068_CR122","doi-asserted-by":"publisher","first-page":"584","DOI":"10.3390\/diabetology5060042","volume":"5","author":"S Rancati","year":"2024","unstructured":"Rancati S, Bosoni P, Schiaffini R, Deodati A, Mongini PA, Sacchi L, et al. Exploration of foundational models for blood glucose forecasting in type-1 diabetes pediatric patients. Diabetology. 2024;5(6):584\u201399.","journal-title":"Diabetology"},{"key":"10068_CR123","doi-asserted-by":"publisher","first-page":"109037","DOI":"10.1016\/j.bspc.2025.109037","volume":"113","author":"AM Di Giacinto","year":"2026","unstructured":"Di Giacinto AM, Petrosino L, D\u2019Antoni F, Marchetti A, Vollero L, Pecchia L, et al. Fedros-randnn: A lightweight online federated learning framework for glycemic forecasting using randomized neural networks. Biomed Signal Process Control. 2026;113:109037.","journal-title":"Biomed Signal Process Control"},{"key":"10068_CR124","doi-asserted-by":"crossref","unstructured":"Kim S-J, Moon JS, Jung S-Y. Long-term blood glucose prediction using deep learning-based noise reduction. ICT Expr. 2025.","DOI":"10.1016\/j.icte.2025.05.009"},{"issue":"1","key":"10068_CR125","doi-asserted-by":"publisher","first-page":"612","DOI":"10.1038\/s41746-025-01994-4","volume":"8","author":"M Hwang","year":"2025","unstructured":"Hwang M, Rachim VP, Yoo J, Lee Y, Park S-M. Generalized multi task learning framework for glucose forecasting and hypoglycemia detection using simulation to reality. NPJ Digit Med. 2025;8(1):612.","journal-title":"NPJ Digit Med"},{"key":"10068_CR126","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ceh.2025.01.003","volume":"8","author":"L Cossu","year":"2025","unstructured":"Cossu L, Prendin F, Cappon G, Herzig D, Bally L, Facchinetti A. Glu4: An open-source package for real-time forecasting and alerting post-bariatric hypoglycemia based on continuous glucose monitoring. Clinic eHealth. 2025;8:1\u20136.","journal-title":"Clinic eHealth"},{"issue":"4","key":"10068_CR127","doi-asserted-by":"publisher","first-page":"255","DOI":"10.3390\/bios15040255","volume":"15","author":"N Chellamani","year":"2025","unstructured":"Chellamani N, Albelwi SA, Shanmuganathan M, Amirthalingam P, Paul A. Diabetes: Non-invasive blood glucose monitoring using federated learning with biosensor signals. Biosens. 2025;15(4):255.","journal-title":"Biosens"},{"key":"10068_CR128","doi-asserted-by":"crossref","unstructured":"Acciaroli G,\u00a0Vettoretti M,\u00a0Facchinetti A,\u00a0Sparacino G. Calibration of cgm systems. In: Glucose monitoring devices. Elsevier; 2020. p. 173\u2013201.","DOI":"10.1016\/B978-0-12-816714-4.00009-0"},{"issue":"11","key":"10068_CR129","doi-asserted-by":"publisher","first-page":"859","DOI":"10.1080\/17434440.2022.2142556","volume":"19","author":"J Ware","year":"2022","unstructured":"Ware J, Hovorka R. Closed-loop insulin delivery: update on the state of the field and emerging technologies. Expert Rev Med Dev. 2022;19(11):859\u201375.","journal-title":"Expert Rev Med Dev"},{"issue":"1","key":"10068_CR130","doi-asserted-by":"publisher","first-page":"e0317662","DOI":"10.1371\/journal.pone.0317662","volume":"20","author":"YF Zhao","year":"2025","unstructured":"Zhao YF, Chaw JK, Ang MC, Tew Y, Shi XY, Liu L, et al. A safe-enhanced fully closed-loop artificial pancreas controller based on deep reinforcement learning. PLoS ONE. 2025;20(1):e0317662.","journal-title":"PLoS ONE"},{"issue":"9","key":"10068_CR131","doi-asserted-by":"publisher","first-page":"e0274608","DOI":"10.1371\/journal.pone.0274608","volume":"17","author":"P Viroonluecha","year":"2022","unstructured":"Viroonluecha P, Egea-Lopez E, Santa J. Evaluation of blood glucose level control in type 1 diabetic patients using deep reinforcement learning. PLoS ONE. 2022;17(9):e0274608.","journal-title":"PLoS ONE"},{"key":"10068_CR132","doi-asserted-by":"crossref","unstructured":"Rakshitha D,\u00a0Lorensuhewa P,\u00a0Nanayakkara D,\u00a0Hettiarachchi C,\u00a0Chitraranjan C. Improving safety in reinforcement learning-based artificial pancreas systems. 2025.","DOI":"10.21203\/rs.3.rs-7810996\/v1"},{"issue":"4","key":"10068_CR133","doi-asserted-by":"publisher","first-page":"e245","DOI":"10.1016\/S2589-7500(22)00020-6","volume":"4","author":"J Ware","year":"2022","unstructured":"Ware J, Boughton CK, Allen JM, Wilinska ME, Tauschmann M, Denvir L, et al. Cambridge hybrid closed-loop algorithm in children and adolescents with type 1 diabetes: a multicentre 6-month randomised controlled trial. Lancet Digit Health. 2022;4(4):e245\u201355.","journal-title":"Lancet Digit Health"},{"issue":"7","key":"10068_CR134","doi-asserted-by":"publisher","first-page":"649","DOI":"10.1016\/j.ifacol.2022.07.517","volume":"55","author":"MC Serafini","year":"2022","unstructured":"Serafini MC, Rosales N, Garelli F. Long-term adaptation of closed-loop glucose regulation via reinforcement learning tools. IFAC-PapersOnLine. 2022;55(7):649\u201354.","journal-title":"IFAC-PapersOnLine"},{"issue":"10","key":"10068_CR135","doi-asserted-by":"publisher","first-page":"5087","DOI":"10.1109\/JBHI.2023.3303367","volume":"27","author":"T Zhu","year":"2023","unstructured":"Zhu T, Li K, Georgiou P. Offline deep reinforcement learning and off-policy evaluation for personalized basal insulin control in type 1 diabetes. IEEE J Biomed Health Inform. 2023;27(10):5087\u201398.","journal-title":"IEEE J Biomed Health Inform"},{"issue":"1","key":"10068_CR136","doi-asserted-by":"publisher","first-page":"47","DOI":"10.3390\/bios16010047","volume":"16","author":"S Huang","year":"2026","unstructured":"Huang S, Fu Y, Kong S, Liu Y, Yan J. A hybrid closed-loop blood glucose control algorithm with a safety limiter based on deep reinforcement learning and model predictive control. Biosensors. 2026;16(1):47.","journal-title":"Biosensors"},{"key":"10068_CR137","doi-asserted-by":"crossref","unstructured":"Panagiotou M,\u00a0Brigato L,\u00a0Streit V,\u00a0Hayoz A,\u00a0Proennecke S,\u00a0Athanasopoulos S, Olsen MT, Den\u00a0Brok EJ, Svensson CH,\u00a0Makrilakis K, et\u00a0al. Personalised insulin adjustment with reinforcement learning: an in-silico validation for people with diabetes on intensive insulin treatment. IEEE Access. 2025.","DOI":"10.2139\/ssrn.4994103"},{"key":"10068_CR138","doi-asserted-by":"publisher","first-page":"104376","DOI":"10.1016\/j.jbi.2023.104376","volume":"142","author":"H Emerson","year":"2023","unstructured":"Emerson H, Guy M, McConville R. Offline reinforcement learning for safer blood glucose control in people with type 1 diabetes. J Biomed Inform. 2023;142:104376.","journal-title":"J Biomed Inform"},{"key":"10068_CR139","doi-asserted-by":"publisher","first-page":"110147","DOI":"10.1016\/j.compbiomed.2025.110147","volume":"191","author":"A Marchetti","year":"2025","unstructured":"Marchetti A, Sasso D, D\u2019Antoni F, Morandin F, Parton M, Matarrese MAG, et al. Deep reinforcement learning for type 1 diabetes: Dual ppo controller for personalized insulin management. Comput Biol Med. 2025;191:110147.","journal-title":"Comput Biol Med"},{"key":"10068_CR140","doi-asserted-by":"publisher","first-page":"105 756","DOI":"10.1109\/ACCESS.2021.3100007","volume":"9","author":"MH Lim","year":"2021","unstructured":"Lim MH, Lee WH, Jeon B, Kim S. A blood glucose control framework based on reinforcement learning with safety and interpretability: In silico validation. IEEE Access. 2021;9:105 756-105 775.","journal-title":"IEEE Access"},{"key":"10068_CR141","doi-asserted-by":"publisher","first-page":"104492","DOI":"10.1016\/j.bspc.2022.104492","volume":"81","author":"S Del Giorno","year":"2023","unstructured":"Del Giorno S, D\u2019Antoni F, Piemonte V, Merone M. A new glycemic closed-loop control based on dyna-q for type-1-diabetes. Biomed Signal Process Control. 2023;81:104492.","journal-title":"Biomed Signal Process Control"},{"key":"10068_CR142","doi-asserted-by":"crossref","unstructured":"Zimmermann D, Kaltenbach H-M. Reducing exercise-related hypoglycemia in automated insulin delivery with reinforcement learning. IFAC J Syst Contr. 2025;100337.","DOI":"10.1016\/j.ifacsc.2025.100337"},{"issue":"4","key":"10068_CR143","doi-asserted-by":"publisher","first-page":"1223","DOI":"10.1109\/JBHI.2020.3014556","volume":"25","author":"T Zhu","year":"2020","unstructured":"Zhu T, Li K, Herrero P, Georgiou P. Basal glucose control in type 1 diabetes using deep reinforcement learning: An in silico validation. IEEE J Biomed Health Inform. 2020;25(4):1223\u201332.","journal-title":"IEEE J Biomed Health Inform"},{"key":"10068_CR144","doi-asserted-by":"publisher","first-page":"105762","DOI":"10.1016\/j.conengprac.2023.105762","volume":"142","author":"M Jaloli","year":"2024","unstructured":"Jaloli M, Cescon M. Basal-bolus advisor for type 1 diabetes (t1d) patients using multi-agent reinforcement learning (rl) methodology. Control Eng Pract. 2024;142:105762.","journal-title":"Control Eng Pract"},{"issue":"17","key":"10068_CR145","doi-asserted-by":"publisher","first-page":"1566","DOI":"10.1056\/NEJMoa2303911","volume":"389","author":"TT Lee","year":"2023","unstructured":"Lee TT, Collett C, Bergford S, Hartnell S, Scott EM, Lindsay RS, et al. Automated insulin delivery in women with pregnancy complicated by type 1 diabetes. N Engl J Med. 2023;389(17):1566\u201378.","journal-title":"N Engl J Med"},{"issue":"9","key":"10068_CR146","doi-asserted-by":"publisher","first-page":"635","DOI":"10.1089\/dia.2022.0084","volume":"24","author":"JE Pinsker","year":"2022","unstructured":"Pinsker JE, Dassau E, Deshpande S, Raghinaru D, Buckingham BA, Kudva YC, et al. Outpatient randomized crossover comparison of zone model predictive control automated insulin delivery with weekly data driven adaptation versus sensor-augmented pump: results from the international diabetes closed-loop trial 4. Diabet Technol Therapeut. 2022;24(9):635\u201342.","journal-title":"Diabet Technol Therapeut"},{"issue":"12","key":"10068_CR147","doi-asserted-by":"publisher","first-page":"856","DOI":"10.1089\/dia.2023.0262","volume":"25","author":"M Nwokolo","year":"2023","unstructured":"Nwokolo M, Lakshman R, Hartnell S, Alwan H, Ware J, Allen JM, et al. Camaps fx hybrid closed-loop with ultra-rapid lispro compared with standard lispro in adults with type 1 diabetes: a double-blind, randomized, crossover study. Diabet Technol Therapeut. 2023;25(12):856\u201363.","journal-title":"Diabet Technol Therapeut"},{"issue":"3","key":"10068_CR148","doi-asserted-by":"publisher","first-page":"844","DOI":"10.2337\/dc20-1809","volume":"44","author":"C Amadou","year":"2021","unstructured":"Amadou C, Franc S, Benhamou P-Y, Lablanche S, Huneker E, Charpentier G, et al. Diabeloop dblg1 closed-loop system enables patients with type 1 diabetes to significantly improve their glycemic control in real-life situations without serious adverse events: 6-month follow-up. Diabetes Care. 2021;44(3):844\u20136.","journal-title":"Diabetes Care"},{"issue":"2","key":"10068_CR149","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1089\/dia.2021.0203","volume":"24","author":"JD Silva","year":"2022","unstructured":"Silva JD, Lepore G, Battelino T, Arrieta A, Casta\u00f1eda J, Grossman B, et al. Real-world performance of the minimed\u2122 780g system: first report of outcomes from 4120 users. Diabet Technol Therapeut. 2022;24(2):113\u20139.","journal-title":"Diabet Technol Therapeut"},{"key":"10068_CR150","doi-asserted-by":"crossref","unstructured":"Wilkinson TM, de Bock MI, Meier R, Hurd S, Sasson-Katchalski R, Trahan A, et al. Fully closed-loop insulin delivery with high-carbohydrate and high-fat meals using the tandem freedom system. J Diabetes Sci Technol. 2025;19322968251389966.","DOI":"10.1177\/19322968251389966"},{"issue":"5","key":"10068_CR151","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1089\/dia.2020.0507","volume":"23","author":"L Ekhlaspour","year":"2021","unstructured":"Ekhlaspour L, Schoelwer MJ, Forlenza GP, DeBoer MD, Norlander L, Hsu L, et al. Safety and performance of the tandem t: slim x2 with control-iq automated insulin delivery system in toddlers and preschoolers. Diabet Technol Therapeut. 2021;23(5):384\u201391.","journal-title":"Diabet Technol Therapeut"},{"issue":"1","key":"10068_CR152","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1177\/19322968211060074","volume":"16","author":"R Armiger","year":"2022","unstructured":"Armiger R, Reddy M, Oliver NS, Georgiou P, Herrero P. An in silico head-to-head comparison of the do-it-yourself artificial pancreas loop and bio-inspired artificial pancreas control algorithms. J Diabetes Sci Technol. 2022;16(1):29\u201339.","journal-title":"J Diabetes Sci Technol"},{"key":"10068_CR153","doi-asserted-by":"publisher","first-page":"105936","DOI":"10.1016\/j.cmpb.2021.105936","volume":"200","author":"A Jafar","year":"2021","unstructured":"Jafar A, El Fathi A, Haidar A. Long-term use of the hybrid artificial pancreas by adjusting carbohydrate ratios and programmed basal rate: A reinforcement learning approach. Comput Methods Programs Biomed. 2021;200:105936.","journal-title":"Comput Methods Programs Biomed"},{"issue":"2","key":"10068_CR154","doi-asserted-by":"publisher","first-page":"536","DOI":"10.1109\/JBHI.2020.3002022","volume":"25","author":"S Lee","year":"2020","unstructured":"Lee S, Kim J, Park SW, Jin S-M, Park S-M. Toward a fully automated artificial pancreas system using a bioinspired reinforcement learning design: In silico validation. IEEE J Biomed Health Inform. 2020;25(2):536\u201346.","journal-title":"IEEE J Biomed Health Inform"},{"key":"10068_CR155","doi-asserted-by":"publisher","first-page":"102436","DOI":"10.1016\/j.artmed.2022.102436","volume":"134","author":"S Ahmad","year":"2022","unstructured":"Ahmad S, Beneyto A, Contreras I, Vehi J. Bolus insulin calculation without meal information. a reinforcement learning approach. Artif Intell Med. 2022;134:102436.","journal-title":"Artif Intell Med"},{"issue":"40","key":"10068_CR156","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.ifacol.2023.01.058","volume":"55","author":"F Di Felice","year":"2022","unstructured":"Di Felice F, Borri A, Di Benedetto MD. Deep reinforcement learning for closed-loop blood glucose control: two approaches. IFAC-PapersOnLine. 2022;55(40):115\u201320.","journal-title":"IFAC-PapersOnLine"},{"key":"10068_CR157","doi-asserted-by":"publisher","first-page":"120156","DOI":"10.1016\/j.eswa.2023.120156","volume":"228","author":"X Yu","year":"2023","unstructured":"Yu X, Guan Y, Yan L, Li S, Fu X, Jiang J. Arlpe: A meta reinforcement learning framework for glucose regulation in type 1 diabetics. Expert Syst Appl. 2023;228:120156.","journal-title":"Expert Syst Appl"},{"key":"10068_CR158","doi-asserted-by":"publisher","first-page":"105839","DOI":"10.1016\/j.bspc.2023.105839","volume":"90","author":"C Hettiarachchi","year":"2024","unstructured":"Hettiarachchi C, Malagutti N, Nolan CJ, Suominen H, Daskalaki E. G2p2c\u2014a modular reinforcement learning algorithm for glucose control by glucose prediction and planning in type 1 diabetes. Biomed Signal Process Control. 2024;90:105839.","journal-title":"Biomed Signal Process Control"},{"key":"10068_CR159","unstructured":"Osband I,\u00a0Blundell C,\u00a0Pritzel A,\u00a0Van\u00a0Roy B. Deep exploration via bootstrapped dqn. Adv Neural Inf Process Syst. 2016;29."},{"key":"10068_CR160","unstructured":"Schulman J,\u00a0Wolski F,\u00a0Dhariwal P,\u00a0Radford A,\u00a0Klimov O. Proximal policy optimization algorithms. 2017. arXiv:1707.06347"},{"key":"10068_CR161","unstructured":"Haarnoja T,\u00a0Zhou A,\u00a0Abbeel P,\u00a0Levine S. Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. In: International conference on machine learning. Pmlr; 2018. p. 1861\u20131870."},{"key":"10068_CR162","first-page":"27\u00a0475","volume":"34","author":"Y Chandak","year":"2021","unstructured":"Chandak Y, Niekum S, da Silva B, Learned-Miller E, Brunskill E, Thomas PS. Universal off-policy evaluation. Adv Neural Inf Process Syst. 2021;34:27\u00a0475-27\u00a0490.","journal-title":"Adv Neural Inf Process Syst"},{"key":"10068_CR163","doi-asserted-by":"crossref","unstructured":"Maqsood S, Sarwar MA,\u00a0Belousovien\u0117 E,\u00a0Maskeli\u016bnas R. Trustworthy glucose forecasting with physiology-constrained neural dynamics, conformal risk control, and risk-sensitive reinforcement learning. IEEE Access. 2026.","DOI":"10.1109\/ACCESS.2026.3672553"},{"key":"10068_CR164","unstructured":"Angelopoulos AN,\u00a0Bates S. A gentle introduction to conformal prediction and distribution-free uncertainty quantification. 2021. arXiv:2107.07511"},{"key":"10068_CR165","doi-asserted-by":"crossref","unstructured":"Sarwar MA, Maqsood S, Amini A, Abbas I, Belousoviene E, Maskeli\u016bnas R. Explainable multitask graph network for blood glucose forecasting and hypoglycemia risk classification. In: 2026 IEEE International Systems Conference (SysCon). IEEE; 2026. p. 1\u20138.","DOI":"10.1109\/SysCon66367.2026.11503533"},{"key":"10068_CR166","doi-asserted-by":"crossref","unstructured":"Sarwar MA,\u00a0Dama\u0161evi\u010dius R,\u00a0Belousovien\u0117 E,\u00a0Maskeli\u016bnas R. Systematic review of artificial intelligence-based methods for glycemic control and risk prediction in intensive care units. Artif Intell Med. 2026;103409.","DOI":"10.1016\/j.artmed.2026.103409"},{"issue":"1","key":"10068_CR167","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3150\/21-BEJ1447","volume":"29","author":"M Fontana","year":"2023","unstructured":"Fontana M, Zeni G, Vantini S. Conformal prediction: a unified review of theory and new challenges. Bernoulli. 2023;29(1):1\u201323.","journal-title":"Bernoulli"},{"issue":"3","key":"10068_CR168","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1002\/bjs.9736","volume":"102","author":"GS Collins","year":"2015","unstructured":"Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (tripod): the tripod statement. J British Surg. 2015;102(3):148\u201358.","journal-title":"J British Surg"},{"key":"10068_CR169","doi-asserted-by":"crossref","unstructured":"7. diabetes technology: standards of care in diabetes\u20142025. Diabet Care. 2025;48(Supplement_1):S146\u2013S166.","DOI":"10.2337\/dc25-S007"},{"issue":"3","key":"10068_CR170","doi-asserted-by":"publisher","first-page":"100231","DOI":"10.1016\/j.mcpdig.2025.100231","volume":"3","author":"V Singh","year":"2025","unstructured":"Singh V, Cheng S, Kwan AC, Ebinger J. United states food and drug administration regulation of clinical software in the era of artificial intelligence and machine learning. Mayo Clinic Proceed Digit Health. 2025;3(3):100231.","journal-title":"Mayo Clinic Proceed Digit Health"},{"key":"10068_CR171","doi-asserted-by":"publisher","first-page":"e76854","DOI":"10.2196\/76854","volume":"4","author":"E Carvalho","year":"2025","unstructured":"Carvalho E, Mascarenhas M, Pinheiro F, Correia R, Balseiro S, Barbosa G, et al. Predetermined change control plans: Guiding principles for advancing safe, effective, and high-quality ai-ml technologies. JMIR AI. 2025;4:e76854.","journal-title":"JMIR AI"}],"container-title":["Reviews in Endocrine and Metabolic Disorders"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11154-026-10068-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11154-026-10068-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11154-026-10068-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T08:02:36Z","timestamp":1783584156000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11154-026-10068-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,9]]},"references-count":171,"alternative-id":["10068"],"URL":"https:\/\/doi.org\/10.1007\/s11154-026-10068-z","relation":{},"ISSN":["1389-9155","1573-2606"],"issn-type":[{"value":"1389-9155","type":"print"},{"value":"1573-2606","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,9]]},"assertion":[{"value":"17 March 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 June 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 July 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}