23 Evaluación del desempeño de Chat GPT-4o para el diagnóstico y clasificación de Retinopatía Diabética BIBLIOGRAFÍA 1. Covarrubias, Trinidad, Delgado, Iris, Rojas, Daniel, & Coria, Marcelo. (2017). Tamizaje en el diagnóstico y prevalencia de retinopatía diabética en atención primaria. Revista médica de Chile, 145(5), 564-571. https://dx.doi.org/10.4067/ S0034-98872017000500002 2. Ruta LM, Magliano DJ, Lemesurier R, Taylor HR, Zimmet PZ, Shaw JE. Prevalence of diabetic retinopathy in Type 2 diabetes in developing and developed countries. Diabet. Med. 2013;30:387– 98. 3. Cheung, N., Mitchell, P., & Wong, T. Y. (2010). Diabetic retinopathy. The Lancet, 376(9735), 124–136. https://doi.org/10.1016/S01406736(09)62124-3 4. Echouffo-Tcheugui J, Ali M, Roglic G, Hayward R, Narayan K. Screening intervals for diabetic retinopathy and incidence of visual loss: a systematic review. Diabet Med 2013; 30: 1272-92 5. Rajesh AE, Davidson OQ, Lee CS, Lee AY: Artificial intelligence and diabetic retinopathy: AI framework, prospective studies, head-tohead validation, and cost-effectiveness. Diabetes Care. 2023, 46:1728-39. 10.2337/dci23-0032 6. 6. Antaki, F., Touma, S., Milad, D., El-Khoury, J., & Duval, R. (2023). Evaluating the performance of ChatGPT in ophthalmology. Ophthalmology Science, 3(4), 100324. https://doi.org/10.1016/j. xops.2023.100324 7. Huang, A. S., Hirabayashi, K., Barna, L., Parikh, D., & Pasquale, L. R. (2024). Assessment of a large language model’s responses to questions and cases about glaucoma and retina management. JAMA Ophthalmology, 142(4), 371–375. https:// doi.org/10.1001/jamaophthalmol.2023.6917 8. Mihalache, A., Huang, R. S., Popovic, M. M., Patil, N. S., Pandya, B. U., Shor, R., Pereira, A., Kwok, J. M., Yan, P., Wong, D. T., Kertes, P. J., & Muni, R. H. (2024). Accuracy of an artificial intelligence chatbot’s interpretation of clinical ophthalmic images. JAMA Ophthalmology, 142(4), 321–326. https://doi.org/10.1001/ jamaophthalmol.2024.0017 9. American Academy of Ophthalmology. (2019). Diabetic retinopathy preferred practice pattern guidelines. Elsevier Inc. https://doi.org/10.1016/j. ophtha.2019.09.025 10. Gupta, A., & Al-Kazwini, H. (2024). Evaluating ChatGPT’s diagnostic accuracy in detecting fundus images. Cureus, 16(11), e73660. https:// doi.org/10.7759/cureus.73660 el fin de identificar correctamente los pacientes en riesgo de pérdida visual significativa que requieren tratamiento activo. Fernendo Espinoza C. Mail: f.espinozacastillo@gmail.com Dirección: Las Hortensias 2759, depto. 702. Providencia. Santiago Cel: +569 64956134
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