Evidence map›Paper›PMID 41038974›Full record

ArticleNPJ digital medicine2025

Current status and solutions for AI ethics in ophthalmology: a bibliometric analysis.

Xinwei Chen, Yahan Yang, Dongyuan Yun, Ruixin Wang, Yuanfan Lin, Mingjie Luo, Weixing Zhang, Patrick Yu-Wai-Man, Matthew Burton, Lingyi Liang and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
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

11 authors.

Xinwei Chen *Zhongshan Ophthalmic Center, Sun Yat-sen University, WHO Collaborating Centre for Eye Care and Vision, State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China.
Yahan Yang *Zhongshan Ophthalmic Center, Sun Yat-sen University, WHO Collaborating Centre for Eye Care and Vision, State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China. yangyh39@mail3.sysu.edu.cn.
Dongyuan Yun *Zhongshan Ophthalmic Center, Sun Yat-sen University, WHO Collaborating Centre for Eye Care and Vision, State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China.
Ruixin WangZhongshan Ophthalmic Center, Sun Yat-sen University, WHO Collaborating Centre for Eye Care and Vision, State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China.
Yuanfan LinZhongshan Ophthalmic Center, Sun Yat-sen University, WHO Collaborating Centre for Eye Care and Vision, State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China.
Mingjie LuoZhongshan Ophthalmic Center, Sun Yat-sen University, WHO Collaborating Centre for Eye Care and Vision, State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China.
Weixing ZhangZhongshan Ophthalmic Center, Sun Yat-sen University, WHO Collaborating Centre for Eye Care and Vision, State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China.
Patrick Yu-Wai-ManCambridge Center for Brain Repair and MRC Mitochondrial Biology Unit, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
Matthew BurtonInternational Centre for Eye Health, London School of Hygiene and Tropical Medicine Department of Clinical Research, London, UK.
Lingyi LiangZhongshan Ophthalmic Center, Sun Yat-sen University, WHO Collaborating Centre for Eye Care and Vision, State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China.
Haotian LinZhongshan Ophthalmic Center, Sun Yat-sen University, WHO Collaborating Centre for Eye Care and Vision, State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China. linht5@mail.sysu.edu.cn.

Funding

China Postdoctoral Science Foundation 2023M734047National Natural Science Foundation of China 82301256National Natural Science Foundation of China 82301265National Natural Science Foundation of China 82441003Science and Technology Planning Project of Guangdong Province 2023A1111120011Young Elite Scientists Sponsorship Program by CAST 2024QNRC001
6 · The paper itself

Abstract

Ophthalmology, an early adopter of medical artificial intelligence (AI), has made significant advancements, but ethical discussions limited. This bibliometric analysis reviewed 498 publications from Web of Science and Scopus (2000-2023) to explore the evolution, status, thematic trends, data modalities and their ethical focus, and solutions in ophthalmic AI ethics. Ophthalmology ranks second in medical AI ethics, with major contributors from the United States (US), China, the United Kingdom (UK), Singapore and India. Ethical concerns focus on privacy, fairness, and transparency. Ethical priorities differ by modality with fundus imaging (59.4%) and Optical Coherence Tomography (OCT) (30.9%) as the most common. Most studies (78.3%) address ethics in diagnostic algorithm development, while only 11.5% directly target ethical concerns, though this focus is increasing. This study highlights the need for AI technologies and guidelines to address ethical challenges in ophthalmology, offering a future direction for research and innovation in medical AI governance.

Identifiers

PMID41038974
PMCPMC12491592

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.