Evidence map›Paper›PMID 42290693›Full record

ArticleFrontiers in artificial intelligence2026

Assessing GPT-4o in cataract surgery decision-making: appropriateness, consistency, and clinical implications.

Yan Liu, Chaozhong Zhang, Yuanping Yuan, Kalibinur Elken, Jie Lai, Yinuo Wen, Linghao Song, Xinyue Wang, Min Zhang, Zexu Chen and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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. Review
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

14 authors.

Yan Liu *Eye Institute and Department of Ophthalmology, Eye and ENT Hospital, Fudan University, Shanghai, China.
Chaozhong Zhang *Department of Cataracts, Affiliated Eye Hospital of Nanchang University, Nanchang, China.
Yuanping Yuan *Central Hospital of Xinyu Iron and Steel Group, Xinyu, China.
Kalibinur ElkenDepartment of Cataracts, Second People's Hospital of Kashgar, Kashgar, China.
Jie LaiEye Institute and Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, Nanchang, China.
Yinuo WenEye Institute and Department of Ophthalmology, Eye and ENT Hospital, Fudan University, Shanghai, China.
Linghao SongEye Institute and Department of Ophthalmology, Eye and ENT Hospital, Fudan University, Shanghai, China.
Xinyue WangEye Institute and Department of Ophthalmology, Eye and ENT Hospital, Fudan University, Shanghai, China.
Min ZhangEye Institute and Department of Ophthalmology, Eye and ENT Hospital, Fudan University, Shanghai, China.
Zexu ChenEye Institute and Department of Ophthalmology, Eye and ENT Hospital, Fudan University, Shanghai, China.
Zijia ZhaoEye Institute and Department of Ophthalmology, Eye and ENT Hospital, Fudan University, Shanghai, China.
Yi ZhangEye Institute and Department of Ophthalmology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China.
Tianhui ChenEye Institute and Department of Ophthalmology, Eye and ENT Hospital, Fudan University, Shanghai, China.
Yongxiang JiangEye Institute and Department of Ophthalmology, Eye and ENT Hospital, Fudan University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: To evaluate the application of large language models (LLMs) in clinical decision-making for cataract surgery using real-world patient data. Methods: This retrospective clinical observational study included 74 cataract patients (146 eyes) who underwent phacoemulsification and intraocular lens (IOL) implantation. Preoperative datasets, including clinical findings from ophthalmic imaging, biometric parameters, systemic histories, and lifestyle questionnaires, were processed via GPT-4o. The model was prompted to simulate an experienced cataract surgeon. GPT-4o's IOL recommendations were evaluated for appropriateness with an adjudicated expert consensus and compared against selections made by high- and low-volume cataract surgeons. Appropriateness rates, agreement (Kappa value), and consistency (intraclass correlation coefficient, ICC) were analyzed. Results: The overall appropriateness rate of GPT-4o for IOL selection was significantly lower than that of human surgeons (37.84% vs. 72.14%, Conclusion: GPT-4o demonstrated moderate appropriateness for routine monofocal IOL selection but showed limited appropriateness and consistency in complex, customized scenarios. These findings suggest GPT-4o could assist routine decision-making in cataract surgery, while highlighting the need for further domain-specific fine-tuning and multimodal optimization for advanced cases.

Indexed as

appropriatenesscataractcataract surgeonGPT-4ointraocular lenslarge language models (LLMs)

Identifiers

PMID42290693
PMCPMC13260408

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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.