Clinical appropriateness of ChatGPT-4.0 and ChatGPT-4.0-mini for patient-facing ophthalmic procedure information

Background: Patients increasingly turn to large language models (LLMs), such as ChatGPT, for medical guidance, yet their reliability in ophthalmology remains understudied. Prior work has addressed readability and patient handout generation, but a comprehensive evaluation of ChatGPT’s procedural accuracy is lacking. Purpose: To evaluate the accuracy, clinical appropriateness, and readability of ChatGPT-4.0 and ChatGPT-4.0-mini in responding to patient-centered questions about six common ophthalmologic procedures. Methods: In March 2025, a cross-sectional qualitative study was conducted using standardized questions across five domains (indications, alternatives, procedural steps, risks, and recovery) for six ophthalmologic procedures: intravitreal injection, selective laser trabeculoplasty, YAG capsulotomy, cataract surgery, LASIK, and vitrectomy for retinal detachment. Responses from ChatGPT-4.0 and 4.0-mini were independently reviewed by two ophthalmology residents and one board-certified attending for accuracy, relevance, and readability. Results: Both models produced generally accurate and coherent descriptions of procedural indications and recovery. However, they frequently omitted key procedural risks, lacked incidence estimates, and failed to address variability in physician technique. Outdated or inappropriate alternatives, such as topical anti-VEGF therapy for age-related macular degeneration, were identified. ChatGPT-4.0 outperformed the 4.0-mini model in content organization, clinical accuracy, and consistency with the standard of care. Conclusion: ChatGPT offers promise as a patient education tool in ophthalmology, particularly for delivering basic procedural overviews. However, current iterations lack the specificity, clinical nuance, and knowledge of treatments necessary for unsupervised patient use. Ongoing validation and physician oversight are essential before broader implementation in patient education.

Authors

Institutions

Publication Details

Journal
Journal of Academic Ophthalmology
Published
2026-08-27
DOI
https://doi.org/10.62199/2475-4757.1349
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Clinical appropriateness of ChatGPT-4.0 and ChatGPT-4.0-mini for patient-facing ophthalmic procedure information

Piero Carletti, Soumil Prasad, Belen Wertheimer, Matthew McSoley et al.
Journal of Academic Ophthalmology
Artificial Intelligence in Healthcare and Education
article

Clinical appropriateness of ChatGPT-4.0 and ChatGPT-4.0-mini for patient-facing ophthalmic procedure information

Piero Carletti, Soumil Prasad, Belen Wertheimer, Matthew McSoley, Zubair Ansari, Nicholas Joseph DeLuca
article en

Abstract

Background: Patients increasingly turn to large language models (LLMs), such as ChatGPT, for medical guidance, yet their reliability in ophthalmology remains understudied. Prior work has addressed readability and patient handout generation, but a comprehensive evaluation of ChatGPT’s procedural accuracy is lacking. Purpose: To evaluate the accuracy, clinical appropriateness, and readability of ChatGPT-4.0 and ChatGPT-4.0-mini in responding to patient-centered questions about six common ophthalmologic procedures. Methods: In March 2025, a cross-sectional qualitative study was conducted using standardized questions across five domains (indications, alternatives, procedural steps, risks, and recovery) for six ophthalmologic procedures: intravitreal injection, selective laser trabeculoplasty, YAG capsulotomy, cataract surgery, LASIK, and vitrectomy for retinal detachment. Responses from ChatGPT-4.0 and 4.0-mini were independently reviewed by two ophthalmology residents and one board-certified attending for accuracy, relevance, and readability. Results: Both models produced generally accurate and coherent descriptions of procedural indications and recovery. However, they frequently omitted key procedural risks, lacked incidence estimates, and failed to address variability in physician technique. Outdated or inappropriate alternatives, such as topical anti-VEGF therapy for age-related macular degeneration, were identified. ChatGPT-4.0 outperformed the 4.0-mini model in content organization, clinical accuracy, and consistency with the standard of care. Conclusion: ChatGPT offers promise as a patient education tool in ophthalmology, particularly for delivering basic procedural overviews. However, current iterations lack the specificity, clinical nuance, and knowledge of treatments necessary for unsupervised patient use. Ongoing validation and physician oversight are essential before broader implementation in patient education.

Journal of Academic OphthalmologyVol. 18(2)
University of Miami (US)
Quality Education
Openalex Percentile: Top 13%
Artificial Intelligence in Healthcare and Education
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.