Lancet, T. Diabetes: a defining disease of the 21st century. Lancet 401, 2087 (2023).
Harding, J. L., Pavkov, M. E., Magliano, D. J., Shaw, J. E. & Gregg, E. W. Global trends in diabetes complications: a review of current evidence. Diabetologia 62, 3–16 (2019).
Rustad, J. K., Musselman, D. L. & Nemeroff, C. B. The relationship of depression and diabetes: pathophysiological and treatment implications. Psychoneuroendocrinology 36, 1276–1286 (2011).
Khaledi, M., Haghighatdoost, F., Feizi, A. & Aminorroaya, A. The prevalence of comorbid depression in patients with type 2 diabetes: an updated systematic review and meta-analysis on huge number of observational studies. Acta Diabetol. 56, 631–650 (2019).
Dal Canto, E. et al. Diabetes as a cardiovascular risk factor: an overview of global trends of macro and micro vascular complications. Eur. J. Prev. Cardiol. 26, 25–32 (2019).
Hills, A. P. et al. Epidemiology and determinants of type 2 diabetes in South Asia. Lancet Diab. Endocrinol. 6, 966–978 (2018).
Powers, M. A. et al. Diabetes self-management education and support in adults with type 2 diabetes: a consensus report of the american diabetes association, the association of diabetes care & education specialists, the academy of nutrition and dietetics, the American Academy of Family Physicians, the American Academy of PAs, the American Association of Nurse Practitioners, and the American Pharmacists Association. Diabetes Care 43, 1636–1649 (2020).
Kupfer, J. M. & Bond, E. U. Patient satisfaction and patient-centered care: necessary but not equal. JAMA 308, 139–140 (2012).
Bierman, A. S. & Tinetti, M. E. Precision medicine to precision care: managing multimorbidity. Lancet 388, 2721–2723 (2016).
Williams, J. S., Walker, R. J., Smalls, B. L., Hill, R. & Egede, L. E. Patient-centered care, glycemic control, diabetes self-care, and quality of life in adults with type 2 diabetes. Diabetes Technol. Ther. 18, 644–649 (2016).
Davies, M. J. et al. Management of hyperglycemia in type 2 diabetes, 2018. a consensus report by the American Diabetes Association and the European Association for the study of diabetes. Diabetes Care 41, 2669–2701 (2018).
Rutten, G. E. H. M., Van Vugt, H. & de Koning, E. Person-centered diabetes care and patient activation in people with type 2 diabetes. BMJ Open Diabetes Res. Care 8, e001926 (2020).
Asmat, K., Dhamani, K., Gul, R. & Froelicher, E. S. The effectiveness of patient-centered care vs. usual care in type 2 diabetes self-management: a systematic review and meta-analysis. Front. Public Health 10, 994766 (2022).
Wade-Vuturo, A. E., Mayberry, L. S. & Osborn, C. Y. Secure messaging and diabetes management: experiences and perspectives of patient portal users. J. Am. Med. Inform. Assoc. 20, 519–525 (2013).
Holmgren, A. J. et al. Assessing the impact of the COVID-19 pandemic on clinician ambulatory electronic health record use. J. Am. Med. Inform. Assoc. 29, 453–460 (2022).
Sun, R., Blayney, D. W. & Hernandez-Boussard, T. Health management via telemedicine: learning from the COVID-19 experience. J. Am. Med. Inform. Assoc. JAMIA 28, 2536–2540 (2021).
Kumar, A. et al. Is diabetes mellitus associated with mortality and severity of COVID-19? A meta-analysis. Diabetes Metab. Syndr. 14, 535–545 (2020).
Brands, M. R. et al. Patient-centered digital health records and their effects on health outcomes: systematic review. J. Med. Internet Res. 24, e43086 (2022).
Sarraju, A. et al. Identifying reasons for statin nonuse in patients with diabetes using deep learning of electronic health records. J. Am. Heart Assoc. 12, e028120 (2023).
Chung, S., Panattoni, L., Chi, J. & Palaniappan, L. Can secure patient-provider messaging improve diabetes care? Diabetes Care 40, 1342–1348 (2017).
Somani, S., van Buchem, M. M., Sarraju, A., Hernandez-Boussard, T. & Rodriguez, F. Artificial intelligence–enabled analysis of statin-related topics and sentiments on social media. JAMA Netw. Open 6, e239747 (2023).
Uncovska, M., Freitag, B., Meister, S. & Fehring, L. Rating analysis and BERTopic modeling of consumer versus regulated mHealth app reviews in Germany. NPJ Digit. Med. 6, 115 (2023).
Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. AI in health and medicine. Nat. Med. 28, 31–38 (2022).
Beam, K. et al. Performance of a large language model on practice questions for the neonatal board examination. JAMA Pediatr. 177, 977–979 (2023).
Strong, E. et al. Chatbot vs medical student performance on free-response clinical reasoning examinations. JAMA Intern. Med. 183, 1028–1030 (2023).
Kim, J., Cai, Z. R., Chen, M. L., Simard, J. F. & Linos, E. Assessing biases in medical decisions via clinician and AI chatbot responses to patient vignettes. JAMA Netw. Open 6, e2338050 (2023).
Martens, T. et al. Effect of continuous glucose monitoring on glycemic control in patients with type 2 diabetes treated with basal insulin: a randomized clinical trial. JAMA 325, 2262–2272 (2021).
Lee, Y.-B. et al. An integrated digital health care platform for diabetes management with ai-based dietary management: 48-week results from a randomized controlled trial. Diabetes Care 46, 959–966 (2023).
Ganguli, I., Orav, E. J., Lupo, C., Metlay, J. P. & Sequist, T. D. Patient and visit characteristics associated with use of direct scheduling in primary care practices. JAMA Netw. Open 3, e209637 (2020).
WHO issues warning on falsified medicines used for diabetes treatment and weight loss. https://www.who.int/news/item/20-06-2024-who-issues-warning-on-falsified-medicines-used-for-diabetes-treatment-and-weight-loss (WHO, 2024).
Manshahia, P. K. et al. Systematic review to gauge the effect of levothyroxine substitution on progression of diabetic nephropathy in patients with hypothyroidism and type 2 diabetes mellitus. Cureus 15, e44729 (2023).
Sellmeyer, D. E. et al. Skeletal metabolism, fracture risk, and fracture outcomes in type 1 and type 2 diabetes. Diabetes 65, 1757–1766 (2016).
Ferrari, S. L. et al. Diagnosis and management of bone fragility in diabetes: an emerging challenge. Osteoporos. Int. 29, 2585–2596 (2018).
Wu, B. et al. A narrative review of diabetic bone disease: characteristics, pathogenesis, and treatment. Front. Endocrinol. 13, 1052592 (2022).
Patel, V., Fancourt, D., Furukawa, T. A. & Kola, L. Reimagining the journey to recovery: the COVID-19 pandemic and global mental health. PLOS Med 20, e1004224 (2023).
Kim, J. et al. Prevalence and associations of poor mental health in the third year of COVID-19: U.S. population-based analysis from 2020 to 2022. Psychiatry Res. 330, 115622 (2023).
Swaminathan, A. et al. Natural language processing system for rapid detection and intervention of mental health crisis chat messages. npj Digit. Med. 6, 1–9 (2023).
Kim, J. et al. Telehealth utilization and associations in the united states during the third year of the covid-19 pandemic: population-based survey study in 2022. JMIR Public Health Surveill. 10, e51279 (2024).
sentence-transformers/all-MiniLM-L6-v2 · Hugging Face. https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2 (2024).
Rousseeuw, P. J. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987).
Davies, D. L. & Bouldin, D. W. A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-1, 224–227 (1979).
Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. BERT: pre-training of deep bidirectional transformers for language understanding. in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) (eds. Burstein, J., Doran, C. & Solorio, T.) 4171–4186 (Association for Computational Linguistics, Minneapolis, Minnesota, 2019). https://doi.org/10.18653/v1/N19-1423.
thenlper/gte-small · Hugging Face. https://huggingface.co/thenlper/gte-small.
Leypold, T., Schäfer, B., Boos, A. & Beier, J. P. Can AI think like a plastic surgeon? Evaluating GPT-4’s clinical judgment in reconstructive procedures of the upper extremity. Plast. Reconstr. Surg. Glob. Open 11, e5471 (2023).
Kojima, T. et al. Large language models are zero-shot reasoners. Adv. Neural Inf. Process. Syst. 35, 22199–22213 (2022).
Artificial intelligence risk management framework (AI RMF 1.0) (NIST, 2023).
Chang, N., Lee-Goldman, R. & Tseng, M. Linguistic wisdom from the crowd. Proc. AAAI Conf. Hum. Comput. Crowdsour. 3, 1–8 (2015).
Subjectivity, ambiguity and disagreement in crowdsourcing. https://www.aconf.org/conf_160152.html (SAD, 2018).