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Large language model (LLM)-driven chatbots for neuro-ophthalmic medical education

Eye volume 38, pages 639–641 (2024)Cite this article

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The subspecialty of neuro-ophthalmology is currently facing a human resource issue globally, with few entering the subspecialty. This has led to access problems for neuro-ophthalmic care worldwide. While the primary goal of ophthalmology is to save a patient’s vision, a good neuro-ophthalmic diagnosis can potentially save lives. Neuro-ophthalmology is a unique subspecialty of ophthalmology that involves going beyond the eye, relying heavily on principles of general medicine but also the skill and expertise of a neuro-ophthalmologist. While becoming a neuro-ophthalmologist can be considered a long and arduous journey, we believe that stronger neuro-ophthalmic expertise can benefit a variety of clinicians, from ophthalmologists to neurosurgeons. In this paper, we examine how GPT-4 can potentially benefit neuro-ophthalmic education.

GPT-4 is a model based on transformers. The model underwent pretraining using an extensive dataset of both publicly available data as well as data procured from third-party providers [1]. During this phase, GPT-4 acquired the ability to anticipate the subsequent token in a sequence [2]. After the pretraining phase, GPT-4 went through a fine-tuning process that employs reinforcement learning methods [2]. The enhances the efficacy of the model, making it more reliable and responsive to various tasks [2]. GPT-4 has already been shown to be useful in writing ophthalmic surgical notes [3]. In contrast to its previous generations which had a primary emphasis on textual inputs, GPT-4 now has the capability to process both textual and visual inputs [4, 5].

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References

  1. Waisberg E, Ong J, Masalkhi M, Zaman N, Sarker P, Lee AG, et al. GPT-4: a new era of artificial intelligence in medicine. Ir J Med Sci. 2023. https://doi.org/10.1007/s11845-023-03377-8.

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Authors and Affiliations

  1. Department of Ophthalmology, University of Cambridge, Cambridge, UK

    Ethan Waisberg & Mouayad Masalkhi

  2. Michigan Medicine, University of Michigan, Ann Arbor, MI, USA

    Joshua Ong

  3. Center for Space Medicine, Baylor College of Medicine, Houston, TX, USA

    Andrew G. Lee

  4. Department of Ophthalmology, Blanton Eye Institute, Houston Methodist Hospital, Houston, TX, USA

    Andrew G. Lee

  5. The Houston Methodist Research Institute, Houston Methodist Hospital, Houston, TX, USA

    Andrew G. Lee

  6. Departments of Ophthalmology, Neurology, and Neurosurgery, Weill Cornell Medicine, New York, NY, USA

    Andrew G. Lee

  7. Department of Ophthalmology, University of Texas Medical Branch, Galveston, TX, USA

    Andrew G. Lee

  8. University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Andrew G. Lee

  9. Texas A&M College of Medicine, Bryan, TX, USA

    Andrew G. Lee

  10. Department of Ophthalmology, The University of Iowa Hospitals and Clinics, Iowa City, IA, USA

    Andrew G. Lee

Authors
  1. Ethan Waisberg
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  2. Joshua Ong
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  3. Mouayad Masalkhi
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  4. Andrew G. Lee
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Contributions

EW: writing, figure development. JO: writing, figure development. MM: writing. and AGL: review, intellectual support.

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Correspondence to Ethan Waisberg.

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Waisberg, E., Ong, J., Masalkhi, M. et al. Large language model (LLM)-driven chatbots for neuro-ophthalmic medical education. Eye 38, 639–641 (2024). https://doi.org/10.1038/s41433-023-02759-7

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