This paper is in the following e-collection/theme issue:

Formative Evaluation of Digital Health Interventions (2993) Chatbots and Conversational Agents (684) Generative Language Models Including ChatGPT (601) Ophthalmology (64) Health Services in Resource-Poor Settings and LMICs (488)

Published on in Vol 9 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/64986, first published .
Enhancing Diagnostic Accuracy of Ophthalmological Conditions With Complex Prompts in GPT-4: Comparative Analysis of Global and Low- and Middle-Income Country (LMIC)–Specific Pathologies

Enhancing Diagnostic Accuracy of Ophthalmological Conditions With Complex Prompts in GPT-4: Comparative Analysis of Global and Low- and Middle-Income Country (LMIC)–Specific Pathologies

Enhancing Diagnostic Accuracy of Ophthalmological Conditions With Complex Prompts in GPT-4: Comparative Analysis of Global and Low- and Middle-Income Country (LMIC)–Specific Pathologies

Authors of this article:

Shona Alex Tapiwa M'gadzah1 Author Orcid Image ;   Andrew O'Malley1 Author Orcid Image
Article Authors Cited by (1) Tweetations (3) Metrics

Shona Alex Tapiwa M'gadzah   1 , BSc ;   Andrew O'Malley   1 , PhD

1 School of Medicine, University of St Andrews, St Andrews, United Kingdom

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