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Abstract
Background We aimed to define the capability of three different publicly available large language models, Chat Generative Pretrained Transformer (ChatGPT-3.5), ChatGPT-4 and Google Gemini in analysing retinal detachment cases and suggesting the best possible surgical planning.
Methods Analysis of 54 retinal detachments records entered into ChatGPT and Gemini’s interfaces. After asking ‘Specify what kind of surgical planning you would suggest and the eventual intraocular tamponade.’ and collecting the given answers, we assessed the level of agreement with the common opinion of three expert vitreoretinal surgeons. Moreover, ChatGPT and Gemini answers were graded 1–5 (from poor to excellent quality), according to the Global Quality Score (GQS).
Results After excluding 4 controversial cases, 50 cases were included. Overall, ChatGPT-3.5, ChatGPT-4 and Google Gemini surgical choices agreed with those of vitreoretinal surgeons in 40/50 (80%), 42/50 (84%) and 35/50 (70%) of cases. Google Gemini was not able to respond in five cases. Contingency analysis showed significant differences between ChatGPT-4 and Gemini (p=0.03). ChatGPT’s GQS were 3.9±0.8 and 4.2±0.7 for versions 3.5 and 4, while Gemini scored 3.5±1.1. There was no statistical difference between the two ChatGPTs (p=0.22), while both outperformed Gemini scores (p=0.03 and p=0.002, respectively). The main source of error was endotamponade choice (14% for ChatGPT-3.5 and 4, and 12% for Google Gemini). Only ChatGPT-4 was able to suggest a combined phacovitrectomy approach.
Conclusion In conclusion, Google Gemini and ChatGPT evaluated vitreoretinal patients’ records in a coherent manner, showing a good level of agreement with expert surgeons. According to the GQS, ChatGPT’s recommendations were much more accurate and precise.
- Retina
- Vitreous
- Medical Education
- Ophthalmologic Surgical Procedures
- Surveys and Questionnaires
Data availability statement
Data are available on reasonable request. The data that support the findings of this study are available from the corresponding author, MMC, on reasonable request.
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Data availability statement
Data are available on reasonable request. The data that support the findings of this study are available from the corresponding author, MMC, on reasonable request.
Footnotes
Contributors Conceptualisation, MMC and GG; methodology, MMC; validation, AB; formal analysis, EC; investigation, MMC; writing—original draft preparation, MMC; writing—review and editing FG, FB and NCD'O; guarantor, MMC; project administration, SR. All authors have read and agreed to the published version of the manuscript.
Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.
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