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Systematic review
Review of emerging trends and projection of future developments in large language models research in ophthalmology
  1. Matthew Wong1,
  2. Zhi Wei Lim2,
  3. Krithi Pushpanathan2,3,
  4. Carol Y Cheung4,
  5. Ya Xing Wang5,
  6. David Chen3,6,
  7. Yih Chung Tham2,3,7
  1. 1 University of Cambridge, Cambridge, UK
  2. 2 Yong Loo Lin School of Medicine, National University of Singapore, Singapore
  3. 3 Centre for Innovation and Precision Eye Health & Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
  4. 4 Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, Hong Kong
  5. 5 Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital University of Medical Science, Beijing, China
  6. 6 Department of Ophthalmology, National University Hospital, Singapore
  7. 7 Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
  1. Correspondence to Dr Yih Chung Tham; thamyc{at}nus.edu.sg

Abstract

Background Large language models (LLMs) are fast emerging as potent tools in healthcare, including ophthalmology. This systematic review offers a twofold contribution: it summarises current trends in ophthalmology-related LLM research and projects future directions for this burgeoning field.

Methods We systematically searched across various databases (PubMed, Europe PMC, Scopus and Web of Science) for articles related to LLM use in ophthalmology, published between 1 January 2022 and 31 July 2023. Selected articles were summarised, and categorised by type (editorial, commentary, original research, etc) and their research focus (eg, evaluating ChatGPT’s performance in ophthalmology examinations or clinical tasks).

Findings We identified 32 articles meeting our criteria, published between January and July 2023, with a peak in June (n=12). Most were original research evaluating LLMs’ proficiency in clinically related tasks (n=9). Studies demonstrated that ChatGPT-4.0 outperformed its predecessor, ChatGPT-3.5, in ophthalmology exams. Furthermore, ChatGPT excelled in constructing discharge notes (n=2), evaluating diagnoses (n=2) and answering general medical queries (n=6). However, it struggled with generating scientific articles or abstracts (n=3) and answering specific subdomain questions, especially those regarding specific treatment options (n=2). ChatGPT’s performance relative to other LLMs (Google’s Bard, Microsoft’s Bing) varied by study design. Ethical concerns such as data hallucination (n=27), authorship (n=5) and data privacy (n=2) were frequently cited.

Interpretation While LLMs hold transformative potential for healthcare and ophthalmology, concerns over accountability, accuracy and data security remain. Future research should focus on application programming interface integration, comparative assessments of popular LLMs, their ability to interpret image-based data and the establishment of standardised evaluation frameworks.

Data availability statement

Data are available in a public, open access repository. Our systematic review used the data in scientific databases, including Scopus, Web of Science (WOS), PubMed and Europe PMC.

https://doi.org/10.1136/bjo-2023-324734

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    Data availability statement

    Data are available in a public, open access repository. Our systematic review used the data in scientific databases, including Scopus, Web of Science (WOS), PubMed and Europe PMC.

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    Footnotes

    • X @Yih Chung Tham

    • MW, ZWL and KP contributed equally.

    • Contributors MW, ZWL, KP, DC and YCT contributed to conceptualisation. MW, ZWL, KP and YCT contributed to methodology, validation and writing–original draft. MW, ZWL, KP, CYC, YXW, DC and YCT contributed to writing–review and editing. MW and KP contributed to visualisation. YCT is guarantor.

    • Funding Dr YCT was supported by the National Medical Research Council of Singapore (NMRC/MOH/ HCSAINV21nov-0001).

    • Competing interests None declared.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.