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Exploring Diagnostic Precision and Triage Proficiency: A Comparative Study of GPT-4 and Bard in Addressing Common Ophthalmic Complaints

Authors :
Roya Zandi
Joseph D. Fahey
Michael Drakopoulos
John M. Bryan
Siyuan Dong
Paul J. Bryar
Ann E. Bidwell
R. Chris Bowen
Jeremy A. Lavine
Rukhsana G. Mirza
Source :
Bioengineering, Vol 11, Iss 2, p 120 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

In the modern era, patients often resort to the internet for answers to their health-related concerns, and clinics face challenges to providing timely response to patient concerns. This has led to a need to investigate the capabilities of AI chatbots for ophthalmic diagnosis and triage. In this in silico study, 80 simulated patient complaints in ophthalmology with varying urgency levels and clinical descriptors were entered into both ChatGPT and Bard in a systematic 3-step submission process asking chatbots to triage, diagnose, and evaluate urgency. Three ophthalmologists graded chatbot responses. Chatbots were significantly better at ophthalmic triage than diagnosis (90.0% appropriate triage vs. 48.8% correct leading diagnosis; p < 0.001), and GPT-4 performed better than Bard for appropriate triage recommendations (96.3% vs. 83.8%; p = 0.008), grader satisfaction for patient use (81.3% vs. 55.0%; p < 0.001), and lower potential harm rates (6.3% vs. 20.0%; p = 0.010). More descriptors improved the accuracy of diagnosis for both GPT-4 and Bard. These results indicate that chatbots may not need to recognize the correct diagnosis to provide appropriate ophthalmic triage, and there is a potential utility of these tools in aiding patients or triage staff; however, they are not a replacement for professional ophthalmic evaluation or advice.

Details

Language :
English
ISSN :
23065354
Volume :
11
Issue :
2
Database :
Directory of Open Access Journals
Journal :
Bioengineering
Publication Type :
Academic Journal
Accession number :
edsdoj.6a086dbe942447c08e28480afd15999c
Document Type :
article
Full Text :
https://doi.org/10.3390/bioengineering11020120