Marijon E, Narayanan K, Smith K, Barra S, Basso C, Blom MT, et al. 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. Lancet. 2023;402:203–34.

Article  Google Scholar 

Sabanayagam C, Banu R, Chee ML, Lee R, Wang YX, Tan G, et al. Incidence and progression of diabetic retinopathy: a systematic review. lancet Diabetes Endocrinol. 2019;7:140–9.

Article  PubMed  Google Scholar 

Group DRSR. Photocoagulation treatment of proliferative diabetic retinopathy: clinical application of Diabetic Retinopathy Study (DRS) findings, DRS Report Number 8. Ophthalmology. 1981;88:583–600.

Article  Google Scholar 

Rahmati M, Smith L, Boyer L, Fond G, Yon DK, Lee H, et al. Factors affecting global adherence for the uptake of diabetic retinopathy screening: A systematic review and meta-analysis. Am J Ophthalmol. 2024;268:94–107.

Article  PubMed  Google Scholar 

Mtuya C, Cleland CR, Philippin H, Paulo K, Njau B, Makupa WU, et al. Reasons for poor follow-up of diabetic retinopathy patients after screening in Tanzania: a cross-sectional study. BMC Ophthalmol. 2016;16:1–7.

Article  Google Scholar 

Davis RM, Fowler S, Bellis K, Pockl J, Al Pakalnis V, Woldorf A. Telemedicine improves eye examination rates in individuals with diabetes: a model for eye-care delivery in underserved communities. Diabetes Care. 2003;26:2476–7.

Article  PubMed  Google Scholar 

Kirkizlar E, Serban N, Sisson JA, Swann JL, Barnes CS, Williams MD. Evaluation of telemedicine for screening of diabetic retinopathy in the Veterans Health Administration. Ophthalmology. 2013;120:2604–10.

Article  PubMed  Google Scholar 

Dow ER, Khan NC, Chen KM, Mishra K, Perera C, Narala R, et al. Artificial Intelligence Improves Patient Follow-Up in a Diabetic Retinopathy Screening Program. Clinical Ophthalmol. 2023;17:3323–30.

Article  Google Scholar 

Huang JJ, Channa R, Wolf RM, Dong Y, Liang M, Wang J, et al. Autonomous artificial intelligence for diabetic eye disease increases access and health equity in underserved populations. NPJ Dig Med. 2024;7:196.

Article  Google Scholar 

Liu J, Gibson E, Ramchal S, Shankar V, Piggott K, Sychev Y, et al. Diabetic retinopathy screening with automated retinal image analysis in a primary care setting improves adherence to ophthalmic care. Ophthalmology Retin. 2021;5:71–77.

Article  Google Scholar 

Mathenge W, Whitestone N, Nkurikiye J, Patnaik JL, Piyasena P, Uwaliraye P, et al. Impact of artificial intelligence assessment of diabetic retinopathy on referral service uptake in a low-resource setting: the RAIDERS randomized trial. Ophthalmology Sci. 2022;2:100168.

Article  Google Scholar 

Wolf RM, Channa R, Liu T, Zehra A, Bromberger L, Patel D, et al. Autonomous artificial intelligence increases screening and follow-up for diabetic retinopathy in youth: the ACCESS randomized control trial. Nature Commun. 2024;15:421.

Article  CAS  Google Scholar 

Wolf RM, Liu T, Thomas C, Prichett L, Zimmer-Galler I, Smith K, et al. The SEE study: safety, efficacy, and equity of implementing autonomous artificial intelligence for diagnosing diabetic retinopathy in youth. Diabetes Care. 2021;44:781–7.

Article  CAS  PubMed  Google Scholar 

Li Z, Keel S, Liu C, He Y, Meng W, Scheetz J, et al. An automated grading system for detection of vision-threatening referable diabetic retinopathy on the basis of color fundus photographs. Diabetes care. 2018;41:2509–16.

Article  PubMed  Google Scholar 

Joseph S, Selvaraj J, Mani I, Kumaragurupari T, Shang X, Mudgil P, et al. Diagnostic accuracy of artificial intelligence based automated diabetic retinopathy screening in real-world settings: a systematic review and meta-analysis. American J Ophthalmol. 2024;263:214–30.

Article  Google Scholar 

Nielsen KB, Lautrup ML, Andersen JK, Savarimuthu TR, Grauslund J. Deep learning–based algorithms in screening of diabetic retinopathy: a systematic review of diagnostic performance. Ophthalmology Retin. 2019;3:294–304.

Article  Google Scholar 

Higgins JP, Thomas J, Chandler J, et al. Cochrane handbook for systematic reviews of interventions. John Wiley & Sons; 2019.

Lee SW, Koo MJ. PRISMA 2020 statement and guidelines for systematic review and meta-analysis articles, and their underlying mathematics: Life Cycle Committee Recommendations. Life Cycle. 2022;2.

Eriksen MB, Frandsen TF. The impact of patient, intervention, comparison, outcome (PICO) as a search strategy tool on literature search quality: a systematic review. Journal Med Libr Assoc: JMLA. 2018;106:420–31. https://doi.org/10.5195/jmla.2018.345.

Article  PubMed  PubMed Central  Google Scholar 

Rahmati M, Molanouri Shamsi M, Woo W, Koyanagi A, Won Lee S, Keon Yon D, et al. Effects of physical rehabilitation interventions in COVID-19 patients following discharge from hospital: A systematic review. Journal Integr Med. 2023;21:149–58.

Article  Google Scholar 

Higgins J. The Cochrane Collaboration’s Tool for Assessing Risk of Bias in Randomised Trials. Cochrane Collaboration. 2011;343:5928.

Google Scholar 

Rahmati M, Fatemi R, Yon DK, Lee SW, Koyanagi A, Il Shin J, et al. The effect of adherence to high-quality dietary pattern on COVID-19 outcomes: A systematic review and meta-analysis. Journal Med Virol. 2023;95:e28298.

Article  CAS  Google Scholar 

Rahmati M, Koyanagi A, Banitalebi E, Yon DK, Lee SW, Il Shin J, et al. The effect of SARS-CoV-2 infection on cardiac function in post-COVID-19 survivors: A systematic review and meta-analysis. Journal Med Virol. 2023;95:e28325.

Article  CAS  Google Scholar 

Rahmati M, Yon DK, Lee SW, Butler L, Koyanagi A, Jacob L, et al. Effects of COVID-19 vaccination during pregnancy on SARS-CoV-2 infection and maternal and neonatal outcomes: A systematic review and meta-analysis. Reviews Med Virol. 2023;33:e2434.

Article  CAS  Google Scholar 

Rahmati M, Yon DK, Lee SW, Udeh R, McEVoy M, Kim MS, et al. New-onset type 1 diabetes in children and adolescents as postacute sequelae of SARS-CoV-2 infection: a systematic review and meta-analysis of cohort studies. Journal Med Virol. 2023;95:e28833.

Article  CAS  Google Scholar 

Rahmati M, Smith L, Boyer L, Fond G, Yon DK, Lee H, et al. Vision impairment and associated daily activity limitation: A systematic review and meta-analysis. PloS one. 2025;20:e0317452.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Rahmati M, Yon DK, Lee SW, Soysal P, Koyanagi A, Jacob L, et al. New-onset neurodegenerative diseases as long-term sequelae of SARS-CoV-2 infection: a systematic review and meta-analysis. Journal Med Virol. 2023;95:e28909.

Article  CAS  Google Scholar 

Rahmati M, McCarthy JJ, Malakoutinia F. Myonuclear permanence in skeletal muscle memory: a systematic review and meta-analysis of human and animal studies. J Cachexia, Sarcopenia Muscle. 2022;13:2276–97.

Article  PubMed  Google Scholar 

Burton MJ, Ramke J, Marques AP, Bourne R, Congdon N, Jones I, et al. The lancet global health commission on global eye health: vision beyond 2020. Lancet Glob Health. 2021;9:e489–e551.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Study VLEGotGBoD. Global estimates on the number of people blind or visually impaired by diabetic retinopathy: a meta-analysis from 2000 to 2020. Eye. 2024;38:2047–57.

Article  Google Scholar 

Khalifa M, Albadawy M. AI in diagnostic imaging: Revolutionising accuracy and efficiency. Computer Methods Prog Biomed Update. 2024;5:100146.

Article  Google Scholar 

Wang Z, Li Z, Li K, Mu S, Zhou X, Di Y. Performance of artificial intelligence in diabetic retinopathy screening: a systematic review and meta-analysis of prospective studies. Frontiers Endocrinol. 2023;14:1197783.

Article  Google Scholar 

Zayed MG, Karsan W, Peto T, Saravanan P, Virgili G, Preiss D. Diabetic retinopathy and quality of life: a systematic review and meta-analysis. JAMA Ophthalmol. 2024;142:199–207.

Article  PubMed  PubMed Central  Google Scholar 

Abràmoff MD, Lavin PT, Birch M, Shah N, Folk JC. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ digital Med. 2018;1:39.

Article  Google Scholar 

Sosale B, Sosale AR, Murthy H, Sengupta S, Naveenam M. Medios–An offline, smartphone-based artificial intelligence algorithm for the diagnosis of diabetic retinopathy. Indian J Ophthalmol. 2020;68:391–5.

Article  PubMed  PubMed Central  Google Scholar 

Rudnicka A, Shakespeare R, Fajtl J, Chambers R, Bolter L, Anderson J, et al. Evaluation of equity in performance of Artificial Intelligence for diabetic retinopathy (DR) detection. Investigative Ophthalmol Vis Sci. 2024;65:4922–4922.

Google Scholar 

Tufail A, Rudisill C, Egan C, Kapetanakis VV, Salas-Vega S, Owen CG, et al. Automated diabetic retinopathy image assessment software: diagnostic accuracy and cost-effectiveness compared with human graders. Ophthalmology. 2017;124:343–51.

Article  PubMed  Google Scholar 

Abramoff MD, Whitestone N, Patnaik JL, Rich E, Ahmed M, Husain L, et al. Autonomous artificial intelligence increases real-world specialist clinic productivity in a cluster-randomized trial. NPJ Digital Med. 2023;6:184.

Article  Google Scholar 

Lawrenson JG, Graham-Rowe E, Lorencatto F, Burr J, Bunce C, Francis JJ, et al. Interventions to increase attendance for diabetic retinopathy screening. Cochrane Database Syst Rev. 2018;1:012054.