Evidence map›Paper›PMID 40062070›Full record

ArticleCureus2025

Accuracy of Artificial Intelligence Versus Clinicians in Real-Life Case Scenarios of Retinopathy of Prematurity.

Akash Belenje, Dhanush Pandya, Subhadra Jalali, Padmaja K Rani

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Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Akash BelenjeSrimati Kanuri Santhamma Center for Vitreo-Retinal Diseases, Anant Bajaj Retina Institute, Kallam Anji Reddy Campus, L V Prasad Eye Institute, Hyderabad, IND.
Dhanush PandyaSrimati Kanuri Santhamma Center for Vitreo-Retinal Diseases, Anant Bajaj Retina Institute, Kallam Anji Reddy Campus, L V Prasad Eye Institute, Hyderabad, IND.
Subhadra JalaliSrimati Kanuri Santhamma Center for Vitreo-Retinal Diseases, Anant Bajaj Retina Institute, Kallam Anji Reddy Campus, L V Prasad Eye Institute, Hyderabad, IND.
Padmaja K RaniSrimati Kanuri Santhamma Center for Vitreo-Retinal Diseases, Anant Bajaj Retina Institute, Kallam Anji Reddy Campus, L V Prasad Eye Institute, Hyderabad, IND.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective The aim of this study was to compare the accuracy of ChatGPT artificial intelligence (AI) with clinicians in real-life case scenarios related to retinopathy of prematurity (ROP). Methods This was a prospectively conducted study with a real-life case scenario-based questionnaire with multiple-response answers. Thirteen clinicians, including eight vitreoretinal fellowship trainees (with less than two years of experience in the management of ROP) and five ROP experts (with more than three years of experience in the management of ROP), were given 10 real-life case scenarios in ROP. The majority of responses from trainees and ROP experts were compared with the ChatGPT AI-generated responses. The ChatGPT exercise was repeated for both versions 3.5 and 4.0 more than a month apart on May 29, 2024, and July 18, 2024, to check for the majority of AI response consistency. For each real-life case scenario, the majority of clinician responses were compared with the majority of AI responses for agreement. Results ChatGPT answered nine cases correctly (90%), outperforming the fellowship trainees (77.5%, i.e., 62 correct responses out of 80). The accuracy of ROP experts was highest at 96% (i.e., 48 correct responses out of 50). There was substantial agreement between the majority of responses of clinicians and the ChatGPT responses, with a Cohen's kappa of 0.80. Conclusion The ChatGPT AI model showed substantial agreement with the majority of clinician responses and performed better than vitreoretinal fellowship trainees. ChatGPT AI presents promising new software tools that can be explored further for use in real-life case scenarios in ROP. A more accurate prompt mentioning the type of screening guidelines can promote more accurate answers by ChatGPT as per the requested guidelines.

Indexed as

artificial intelligencechatgptreal-life case scenariosretinopathy of prematurityvitroretinal fellowship trainees

Identifiers

PMID40062070
PMCPMC11889417

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Registered trials

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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.