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[Preprint]. 2023 Aug 28:2023.08.25.23294635.
doi: 10.1101/2023.08.25.23294635.

Performance of ChatGPT in Diagnosis of Corneal Eye Diseases

Mohammad Delsoz  1 , Yeganeh Madadi  1 , Wuqaas M Munir  2 , Brendan Tamm  2 , Shiva Mehravaran  3 , Mohammad Soleimani  4   5 , Ali Djalilian  4 , Siamak Yousefi  1   6
Affiliations

Performance of ChatGPT in Diagnosis of Corneal Eye Diseases

Mohammad Delsoz et al. medRxiv. .

Update in

Abstract

Introduction: Assessing the capabilities of ChatGPT-4.0 and ChatGPT-3.5 for diagnosing corneal eye diseases based on case reports and compare with human experts.

Methods: We randomly selected 20 cases of corneal diseases including corneal infections, dystrophies, degenerations, and injuries from a publicly accessible online database from the University of Iowa. We then input the text of each case description into ChatGPT-4.0 and ChatGPT3.5 and asked for a provisional diagnosis. We finally evaluated the responses based on the correct diagnoses then compared with the diagnoses of three cornea specialists (Human experts) and evaluated interobserver agreements.

Results: The provisional diagnosis accuracy based on ChatGPT-4.0 was 85% (17 correct out of 20 cases) while the accuracy of ChatGPT-3.5 was 60% (12 correct cases out of 20). The accuracy of three cornea specialists were 100% (20 cases), 90% (18 cases), and 90% (18 cases), respectively. The interobserver agreement between ChatGPT-4.0 and ChatGPT-3.5 was 65% (13 cases) while the interobserver agreement between ChatGPT-4.0 and three cornea specialists were 85% (17 cases), 80% (16 cases), and 75% (15 cases), respectively. However, the interobserver agreement between ChatGPT-3.5 and each of three cornea specialists was 60% (12 cases).

Conclusions: The accuracy of ChatGPT-4.0 in diagnosing patients with various corneal conditions was markedly improved than ChatGPT-3.5 and promising for potential clinical integration.

Keywords: Artificial Intelligence (AI); ChatGPT; Corneal eye diseases; Generative Pre-trained Transformer (GPT); Large Language Models (LLM); Provisional Diagnosis.

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Conflict of interest statement

Conflict of Interest Mohammad Delsoz: None. Yeganeh Madadi: None Wuqaas M Munir: None Brendan Tamm: None Shiva Mehravaran: None Mohammad Soleimani: None Ali Djalilian: None Siamak Yousefi: Remidio, M&S Technologies, Visrtucal Fields, InsihgtAEye, Enolink

Figures

Figure 1.
Figure 1.
A sample case description input into the ChatGPT-4.0 model and corresponding responses.
See this image and copyright information in PMC

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