Evidence map›Paper›PMID 37720035›Full record

ArticlemedRxiv : the preprint server for health sciences2023

Performance of ChatGPT in Diagnosis of Corneal Eye Diseases.

Mohammad Delsoz, Yeganeh Madadi, Wuqaas M Munir, Brendan Tamm, Shiva Mehravaran, Mohammad Soleimani, Ali Djalilian, Siamak Yousefi

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Mohammad DelsozHamilton Eye Institute, Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, TN, USA.
Yeganeh MadadiHamilton Eye Institute, Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, TN, USA.
Wuqaas M MunirDepartment of Ophthalmology and Visual Sciences, University of Maryland School of Medicine, Baltimore, MD, USA.
Brendan TammDepartment of Ophthalmology and Visual Sciences, University of Maryland School of Medicine, Baltimore, MD, USA.
Shiva MehravaranSchool of Computer, Mathematical, and Natural Sciences, Morgan State University, Baltimore, MD, USA.
Mohammad SoleimaniDepartment of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, Illinois, USA.
Ali DjalilianDepartment of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, Illinois, USA.
Siamak YousefiHamilton Eye Institute, Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, TN, USA.

Funding

Predicting the risk of glaucoma from structural, functional, and genetic factors using artificial intelligenceR01EY033005 · NEI · UNIVERSITY OF TENNESSEE HEALTH SCI CTR · PI YOUSEFI, SIAMAK · 2022 to 2025
$2.4M
Improved Glaucoma Monitoring Using Artificial-Intelligence Enabled DashboardR21EY031725 · NEI · UNIVERSITY OF TENNESSEE HEALTH SCI CTR · PI YOUSEFI, SIAMAK · 2020 to 2022
$545k
NEI NIH HHS R01 EY033005NEI NIH HHS R21 EY031725
6 · The paper itself

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.

Indexed as

Artificial Intelligence (AI)ChatGPTCorneal eye diseasesGenerative Pre-trained Transformer (GPT)Large Language Models (LLM)Provisional Diagnosis

Identifiers

PMID37720035
PMCPMC10500623

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.