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DresdenCodak<\/p>\nThere is real AI in the world based on training on real data, especially in medicine. We are starting to see practical AI in every aspect of diabetes of all kinds, brought together in the Lancet paper below. Now I think that we can all agree here that there is too much Artificial Stupidity in the world, aka AI Slop, as though we didn\u2019t have enough of the real thing. We can also agree that thieving data center builders need to be reined in with requirements to provide renewable electricity and industrial heat pump cooling before the computers are turned on.<\/p>\n
I can confidently predict that the world of diabetic quackery will go all in on AI, as it has done on the bogus wristwatch blood glucose sensors that we have occasionally looked at here. I will not get started today on RFK jr. claiming that Type 2 diabetes is \u201ccurable\u201d except to note that he is proof that Natural Stupidity continues to be more malignant than the artificial kinds.<\/p>\n
DresdenCodak<\/p>\n
Can artificial intelligence bridge the gaps for primary diabetes care in low-income and middle-income countries?<\/a><\/p>\n Artificial intelligence (AI) has the potential to improve primary diabetes care in low-income and middle-income countries (LMICs), where the rising burden of disease contrasts sharply with limited health-care resources. Emerging evidence shows the promise of AI for screening, risk prediction, monitoring, and personalised management of diabetes and its complications. However, substantial barriers remain, including infrastructure deficits, data fragmentation, equity and inclusivity challenges, limited prospective validation, and concerns about the acceptability, sustainability, and regulatory oversight of AI. The effective integration of AI into primary diabetes care will depend on coordinated investment in foundational infrastructure that includes large-scale development and rigorous validation of novel AI models for use by primary care physicians and patients across diverse populations. AI initiatives are also needed to support interdisciplinary and international collaborations spanning clinical, technical, and policy domains to ensure successful implementation. By aligning technological innovation with health care needs, AI could evolve from a proof-of-concept tool to a practical enabler of equitable, scalable, and cost-effective diabetes care in LMICs. In this Personal View, we outline the major opportunities and challenges of applying AI to primary diabetes care in LMICs, and propose directions for future development and implementation.<\/p>\n References<\/p>\n The paper above gives these 80 references. Some of these papers are about the nature of the problems we diabetics face around the world, medical, social, technological, and so on, or look at existing datasets to consider what we need for effective AI training to benefit patients and societies. Some, highlighted in bold, particularly take on AI issues.<\/p>\n I trust that it is clear that I have not read all of them yet. This is a growth field, where what is available today will be dwarfed by developments over the next few years.<\/p>\n I have been an avid reader of science fiction since childhood. Nobody whom I know of foresaw the AI boom. I have seen plenty of space travel and robot science fiction, and a few tales that foretold atomic weapons or Global Warming catastrophes or communications satellites in geosynchronous orbits. There are tales of computers like Skynet or Colossus taking over the world, but not practical AI, nor, for that matter, the current wave of Artificial Stupidity polluting the InterWebz. We could talk about a few Heinlein and Asimov tales of beneficent computer-operated governments, but let\u2019s get back to diabetes, where we currently need all the help we can get.<\/p>\n 1.<\/p>\n Chan, JCN \u2219 Lim, LL \u2219 Wareham, NJ \u2219 et al.<\/p>\n The Lancet Commission on diabetes: using data to transform diabetes care and patient lives<\/p>\n Lancet. 2021; 396:2019-2082<\/p>\n Full Text<\/a><\/p>\n Full Text (PDF)<\/a><\/p>\n Scopus (354)<\/a><\/p>\n PubMed<\/a><\/p>\n Google Scholar<\/a><\/p>\n 2.<\/p>\n GBD 2021 Diabetes Collaborators<\/p>\n Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021<\/p>\n Lancet. 2023; 402:203-234<\/p>\n Full Text<\/a><\/p>\n Full Text (PDF)<\/a><\/p>\n Scopus (649)<\/a><\/p>\n PubMed<\/a><\/p>\n Google Scholar<\/a><\/p>\n 3.<\/p>\n NCD Risk Factor Collaboration (NCD-RisC)<\/p>\n Worldwide trends in diabetes prevalence and treatment from 1990 to 2022: a pooled analysis of 1108 population-representative studies with 141 million participants<\/p>\n Lancet. 2024; 404:2077-2093<\/p>\n Full Text<\/a><\/p>\n Full Text (PDF)<\/a><\/p>\n Scopus (0)<\/a><\/p>\n PubMed<\/a><\/p>\n Google Scholar<\/a><\/p>\n 4.<\/p>\n The Lancet Diabetes & Endocrinology<\/p>\n Urbanisation, inequality, and non-communicable disease risk<\/p>\n Lancet Diabetes Endocrinol. 2017; 5:313<\/p>\n Full Text<\/a><\/p>\n Full Text (PDF)<\/a><\/p>\n Scopus (17)<\/a><\/p>\n PubMed<\/a><\/p>\n Google Scholar<\/a><\/p>\n 5.<\/p>\n Global Health and Population Project on Access to Care for Cardiometabolic Diseases Collaborators<\/p>\n Attainment of global diabetes targets in 2021: a pooled analysis of individual-level data from national surveys in 100 low-income, middle-income, and high-income countries<\/p>\n Lancet Glob Health. 2026; 14:e21-e32<\/p>\n Full Text<\/a><\/p>\n Full Text (PDF)<\/a><\/p>\n Scopus (0)<\/a><\/p>\n PubMed<\/a><\/p>\n Google Scholar<\/a><\/p>\n 6.<\/p>\n Beaglehole, R \u2219 Epping-Jordan, J \u2219 Patel, V \u2219 et al.<\/p>\n Improving the prevention and management of chronic disease in low-income and middle-income countries: a priority for primary health care<\/p>\n Lancet. 2008; 372:940-949<\/p>\n Full Text<\/a><\/p>\n Full Text (PDF)<\/a><\/p>\n Scopus (474)<\/a><\/p>\n PubMed<\/a><\/p>\n Google Scholar<\/a><\/p>\n 7.<\/p>\n Global Health and Population Project on Access to Care for Cardiometabolic Diseases<\/p>\n Expanding access to newer medicines for people with type 2 diabetes in low-income and middle-income countries: a cost-effectiveness and price target analysis<\/p>\n Lancet Diabetes Endocrinol. 2021; 9:825-836<\/p>\n Full Text<\/a><\/p>\n Full Text (PDF)<\/a><\/p>\n Scopus (53)<\/a><\/p>\n PubMed<\/a><\/p>\n Google Scholar<\/a><\/p>\n 8.<\/p>\n Agarwal, S \u2219 Wade, AN \u2219 Mbanya, JC \u2219 et al.<\/p>\n The role of structural racism and geographical inequity in diabetes outcomes<\/p>\n Lancet. 2023; 402:235-249<\/p>\n Full Text<\/a><\/p>\n Full Text (PDF)<\/a><\/p>\n Scopus (0)<\/a><\/p>\n PubMed<\/a><\/p>\n Google Scholar<\/a><\/p>\n 9.<\/p>\n Li, J \u2219 Guan, Z \u2219 Wang, J \u2219 et al.<\/p>\n Integrated image-based deep learning and language models for primary diabetes care<\/p>\n Nat Med. 2024; 30:2886-2896<\/p>\n Crossref<\/a><\/p>\n Scopus (0)<\/a><\/p>\n PubMed<\/a><\/p>\n Google Scholar<\/a><\/p>\n 10.<\/p>\n Sheng, B \u2219 Pushpanathan, K \u2219 Guan, Z \u2219 et al.<\/p>\n Artificial intelligence for diabetes care: current and future prospects<\/p>\n Lancet Diabetes Endocrinol. 2024; 12:569-595<\/p>\n Full Text<\/a><\/p>\n Full Text (PDF)<\/a><\/p>\n Scopus (0)<\/a><\/p>\n PubMed<\/a><\/p>\n Google Scholar<\/a><\/p>\n 11.<\/p>\n McCarthy, J \u2219 Minsky, ML \u2219 Shannon, CE<\/p>\n A proposal for the Dartmouth summer research project on artificial intelligence\u2014August 31, 1955<\/p>\n AI Mag. 2006; 27:12-14<\/p>\n Google Scholar<\/a><\/p>\n 12.<\/p>\n Garc\u00eda-Ulloa, AC \u2219 Almeda-Valdes, P \u2219 Aguilar-Salinas, CA \u2219 et al.<\/p>\n Development and validation of a software linked to an internet portal that facilitates the medical treatment and empowerment of patients with type 2 diabetes, interaction with medical personnel, and the generation of a real-time registry<\/p>\n J Diabetes Sci Technol. 2021; 15:525-527<\/p>\n Crossref<\/a><\/p>\n Scopus (0)<\/a><\/p>\n PubMed<\/a><\/p>\n Google Scholar<\/a><\/p>\n 13.<\/p>\n Antonio-Villa, NE \u2219 Palma-Moreno, BG \u2219 Rodr\u00edguez-D\u00e1vila, FM \u2219 et al.<\/p>\n Use of an electronic integral monitoring system for patients with diabetes to identify factors associated with an adequate glycemic goal and to measure quality of care<\/p>\n Prim Care Diabetes. 2021; 15:162-168<\/p>\n Full Text<\/a><\/p>\n Full Text (PDF)<\/a><\/p>\n Scopus (0)<\/a><\/p>\n PubMed<\/a><\/p>\n Google Scholar<\/a><\/p>\n 14.<\/p>\n Zhang, K \u2219 Liu, X \u2219 Xu, J \u2219 et al.<\/p>\n Deep-learning models for the detection and incidence prediction of chronic kidney disease and type 2 diabetes from retinal fundus images<\/p>\n Nat Biomed Eng. 2021; 5:533-545<\/p>\n Crossref<\/a><\/p>\n Scopus (136)<\/a><\/p>\n PubMed<\/a><\/p>\n Google Scholar<\/a><\/p>\n 15.<\/p>\n Ting, DSW \u2219 Cheung, CY \u2219 Lim, G \u2219 et al.<\/p>\n Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes<\/p>\n JAMA. 2017; 318:2211-2223<\/p>\n Crossref<\/a><\/p>\n Scopus (1448)<\/a><\/p>\n PubMed<\/a><\/p>\n Google Scholar<\/a><\/p>\n 16.<\/p>\n Gulshan, V \u2219 Peng, L \u2219 Coram, M \u2219 et al.<\/p>\n Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs<\/p>\n JAMA. 2016; 316:2402-2410<\/p>\n Crossref<\/a><\/p>\n Scopus (5614)<\/a><\/p>\n PubMed<\/a><\/p>\n Google Scholar<\/a><\/p>\n 17.<\/p>\n Meng, Z \u2219 Guan, Z \u2219 Yu, S \u2219 et al.<\/p>\n Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study<\/p>\n Lancet Digit Health. 2025; 7, 100868<\/p>\n PubMed<\/a><\/p>\n Google Scholar<\/a><\/p>\n 18.<\/p>\n Jiang, N \u2219 Ji, H \u2219 Guan, Z \u2219 et al.<\/p>\n A deep learning system for detecting silent brain infarction and predicting stroke risk<\/p>\n Nat Biomed Eng. 2025; 9:1907-1919<\/p>\n Crossref<\/a><\/p>\n Scopus (7)<\/a><\/p>\n PubMed<\/a><\/p>\n Google Scholar<\/a><\/p>\n 19.<\/p>\n Dai, L \u2219 Wu, L \u2219 Li, H \u2219 et al.<\/p>\n A deep learning system for detecting diabetic retinopathy across the disease spectrum<\/p>\n Nat Commun. 2021; 12, 3242<\/p>\n Crossref<\/a><\/p>\n Scopus (243)<\/a><\/p>\n Google Scholar<\/a><\/p>\n 20.<\/p>\n Dai, L \u2219 Sheng, B \u2219 Chen, T \u2219 et al.<\/p>\n A deep learning system for predicting time to progression of diabetic retinopathy<\/p>\n Nat Med. 2024; 30:584-594<\/p>\n Crossref<\/a><\/p>\n Scopus (6)<\/a><\/p>\n PubMed<\/a><\/p>\n Google Scholar<\/a><\/p>\n 21.<\/p>\n Bora, A \u2219 Balasubramanian, S \u2219 Babenko, B \u2219 et al.<\/p>\n Predicting the risk of developing diabetic retinopathy using deep learning<\/p>\n Lancet Digit Health. 2021; 3:e10-e19<\/p>\n Full Text<\/a><\/p>\n Full Text (PDF)<\/a><\/p>\n PubMed<\/a><\/p>\n Google Scholar<\/a><\/p>\n 22.<\/p>\n Lu, Y \u2219 Liu, D \u2219 Liang, Z \u2219 et al.<\/p>\n A pretrained transformer model for decoding individual glucose dynamics from continuous glucose monitoring data<\/p>\n Natl Sci Rev. 2025; 12, nwaf039<\/p>\n Crossref<\/a><\/p>\n Scopus (10)<\/a><\/p>\n Google Scholar<\/a><\/p>\n 23.<\/p>\n Wang, G \u2219 Liu, X \u2219 Ying, Z \u2219 et al.<\/p>\n Optimized glycemic control of type 2 diabetes with reinforcement learning: a proof-of-concept trial<\/p>\n Nat Med. 2023; 29:2633-2642<\/p>\n Crossref<\/a><\/p>\n Scopus (12)<\/a><\/p>\n PubMed<\/a><\/p>\n Google Scholar<\/a><\/p>\n 24.<\/p>\n Tyler, NS \u2219 Mosquera-Lopez, CM \u2219 Wilson, LM \u2219 et al.<\/p>\n An artificial intelligence decision support system for the management of type 1 diabetes<\/p>\n Nat Metab. 2020; 2:612-619<\/p>\n Crossref<\/a><\/p>\n Scopus (95)<\/a><\/p>\n PubMed<\/a><\/p>\n Google Scholar<\/a><\/p>\n 25.<\/p>\n Bergenstal, RM \u2219 Johnson, M \u2219 Passi, R \u2219 et al.<\/p>\n Automated insulin dosing guidance to optimise insulin management in patients with type 2 diabetes: a multicentre, randomised controlled trial<\/p>\n Lancet. 2019; 393:1138-1148<\/p>\n Full Text<\/a><\/p>\n Full Text (PDF)<\/a><\/p>\n Scopus (56)<\/a><\/p>\n PubMed<\/a><\/p>\n Google Scholar<\/a><\/p>\n 26.<\/p>\n Mathioudakis, N \u2219 Lalani, B \u2219 Abusamaan, MS \u2219 et al.<\/p>\n An AI-powered lifestyle intervention vs human coaching in the diabetes prevention program: a randomized clinical trial<\/p>\n JAMA. 2025; 334:2079-2089<\/p>\n Crossref<\/a><\/p>\n Scopus (7)<\/a><\/p>\n PubMed<\/a><\/p>\n Google Scholar<\/a><\/p>\n 27.<\/p>\n Khasentino, J \u2219 Belyaeva, A \u2219 Liu, X \u2219 et al.<\/p>\n A personal health large language model for sleep and fitness coaching<\/p>\n Nat Med. 2025; 31:3394-3403<\/p>\n Crossref<\/a><\/p>\n Scopus (11)<\/a><\/p>\n PubMed<\/a><\/p>\n Google Scholar<\/a><\/p>\n 28.<\/p>\n Ferguson, T \u2219 Olds, T \u2219 Curtis, R \u2219 et al.<\/p>\n Effectiveness of wearable activity trackers to increase physical activity and improve health: a systematic review of systematic reviews and meta-analyses<\/p>\n Lancet Digit Health. 2022; 4:e615-e626<\/p>\n Full Text<\/a><\/p>\n Full Text (PDF)<\/a><\/p>\n PubMed<\/a><\/p>\n Google Scholar<\/a><\/p>\n 29.<\/p>\n Guan, Z \u2219 Li, H \u2219 Liu, R \u2219 et al.<\/p>\n Artificial intelligence in diabetes management: advancements, opportunities, and challenges<\/p>\n Cell Rep Med. 2023; 4, 101213<\/p>\n PubMed<\/a><\/p>\n Google Scholar<\/a><\/p>\n 30.<\/p>\n Qian, B \u2219 Sheng, B \u2219 Chen, H \u2219 et al.<\/p>\n A Competition for the diagnosis of myopic maculopathy by artificial intelligence algorithms<\/p>\n JAMA Ophthalmol. 2024; 142:1006-1015<\/p>\n Crossref<\/a><\/p>\n Scopus (25)<\/a><\/p>\n PubMed<\/a><\/p>\n Google Scholar<\/a><\/p>\n 31.<\/p>\n Agrawal, A<\/p>\n Laying an equitable data foundation for foundation models<\/p>\n Lancet Reg Health Southeast Asia. 2023; 13, 100221<\/p>\n PubMed<\/a><\/p>\n