Enhancing Diagnostic Accuracy of Ophthalmological Conditions with Complex Prompts in GPT-4: A Comparative Analysis of Global and LMIC-Specific Pathologies (dataset)

Description

Ten clinical vignettes representing globally and LMIC-prevalent ophthalmological conditions were presented to GPT-4-0125-preview using simple and complex prompts. Diagnostic performance metrics, including sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were calculated. Statistical comparison between prompts was conducted using a Chi-Square Test of Independence.

Date made available	8 Jul 2025
Publisher	University of St Andrews

Contact

[email protected]

DOI
10.17630/d6649993-7545-4639-91c9-2fff49100458

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ALAN_v_GPT4_Calculations_V2.xlsx
File: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet, 101 KB
Type: Dataset
Licence: CC BY

1 Article

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
M'gadzah, S. A. T. & O'Malley, A., 30 Jun 2025, In: JMIR Formative Research. 9, p. 1-9 9 p., e64986.
Research output: Contribution to journal › Article › peer-review

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Enhancing Diagnostic Accuracy of Ophthalmological Conditions with Complex Prompts in GPT-4: A Comparative Analysis of Global and LMIC-Specific Pathologies (dataset)

Description

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DOI

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Research output

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

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