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Large language models in vitreoretinal surgery

Eye volume 38, pages 809–810 (2024)Cite this article

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Over the last 5 years, artificial intelligence (AI) has seen widespread adoption in various fields, including healthcare, finance, and transportation. This growth can be attributed to significant advancements in AI sub-areas [1]. Large language models (LLMs), which are advanced AI models trained on extensive textual data, have gained immense popularity among the general population and have also impacted patients who increasingly rely on large language models (LLMs) to seek clinical information.

In particular, in vitreoretinal conditions, there is a lack of patient awareness of “alarm” symptoms that may delay presentation for medical attention [2]. In response, patients often turn to the internet, social networks, and, with the new boom in artificial intelligence, also to large language models to find answers to their questions [3].

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References

  1. Michael LL, Ifeoma A, Guy B, Craig B, Morgan C, Finale D-V. et al. Gathering strength, gathering storms: The one hundred year study on artificial intelligence (AI100) 2021 study panel report. Stanford University, Stanford, CA, 2021. http://ai100.stanford.edu/2021-report. Accessed 16 June 2023.

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Authors and Affiliations

  1. Vitreoretinal Unit, Moorfields Eye Hospital NHS Foundation Trust, City Road London, London, EC1V 2PD, UK

    Rodrigo Anguita, Achini Makuloluwa, Jennifer Hind & Louisa Wickham

  2. Department of Ophthalmology, Inselspital, University Hospital of Bern, Bern, Switzerland

    Rodrigo Anguita

Authors
  1. Rodrigo Anguita
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  2. Achini Makuloluwa
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  3. Jennifer Hind
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  4. Louisa Wickham
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Contributions

RA and AM conceived and designed the research. RA, AM, and JH analyzed the data. RA, AM, JH, and LW analyzed and interpreted the literature. RA, AM, JH, and LW drafted the manuscript and made critical revisions of the manuscript.

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Correspondence to Rodrigo Anguita.

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Anguita, R., Makuloluwa, A., Hind, J. et al. Large language models in vitreoretinal surgery. Eye 38, 809–810 (2024). https://doi.org/10.1038/s41433-023-02751-1

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