• Luis Filipe Nakayama, Lucas Zago Ribeiro, Frederico Novaes, Isabele Ayumi Miyawaki, Andresa Emy Miyawaki, Juliana Angélica Estevão de Oliveira, Talita Oliveira, Fernando Korn Malerbi, Caio Vinicius Saito Regatieri, Leo Anthony Celi, and Paolo S Silva.
• Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
• Ann. Med. 2023 Jan 1; 55 (2): 22581492258149.

PurposeThis study aims to compare artificial intelligence (AI) systems applied in diabetic retinopathy (DR) teleophthalmology screening, currently deployed systems, fairness initiatives and the challenges for implementation.MethodsThe review included articles retrieved from PubMed/Medline/EMBASE literature search strategy regarding telemedicine, DR and AI. The screening criteria included human articles in English, Portuguese or Spanish and related to telemedicine and AI for DR screening. The author's affiliations and the study's population income group were classified according to the World Bank Country and Lending Groups.ResultsThe literature search yielded a total of 132 articles, and nine were included after full-text assessment. The selected articles were published between 2004 and 2020 and were grouped as telemedicine systems, algorithms, economic analysis and image quality assessment. Four telemedicine systems that perform a quality assessment, image preprocessing and pathological screening were reviewed. A data and post-deployment bias assessment are not performed in any of the algorithms, and none of the studies evaluate the social impact implementations. There is a lack of representativeness in the reviewed articles, with most authors and target populations from high-income countries and no low-income country representation.ConclusionsTelemedicine and AI hold great promise for augmenting decision-making in medical care, expanding patient access and enhancing cost-effectiveness. Economic studies and social science analysis are crucial to support the implementation of AI in teleophthalmology screening programs. Promoting fairness and generalizability in automated systems combined with telemedicine screening programs is not straightforward. Improving data representativeness, reducing biases and promoting equity in deployment and post-deployment studies are all critical steps in model development.

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