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Comparative Study
. 2024 Sep;262(9):2945-2959.
doi: 10.1007/s00417-024-06470-5. Epub 2024 Apr 4.

Large language models as assistance for glaucoma surgical cases: a ChatGPT vs. Google Gemini comparison

Matteo Mario Carlà  1   2 , Gloria Gambini  3   4 , Antonio Baldascino  3   4 , Francesco Boselli  3   4 , Federico Giannuzzi  3   4 , Fabio Margollicci  3   4 , Stanislao Rizzo  3   4
Affiliations
Comparative Study

Large language models as assistance for glaucoma surgical cases: a ChatGPT vs. Google Gemini comparison

Matteo Mario Carlà et al. Graefes Arch Clin Exp Ophthalmol. 2024 Sep.

Abstract

Purpose: The aim of this study was to define the capability of ChatGPT-4 and Google Gemini in analyzing detailed glaucoma case descriptions and suggesting an accurate surgical plan.

Methods: Retrospective analysis of 60 medical records of surgical glaucoma was divided into "ordinary" (n = 40) and "challenging" (n = 20) scenarios. Case descriptions were entered into ChatGPT and Bard's interfaces with the question "What kind of surgery would you perform?" and repeated three times to analyze the answers' consistency. After collecting the answers, we assessed the level of agreement with the unified opinion of three glaucoma surgeons. Moreover, we graded the quality of the responses with scores from 1 (poor quality) to 5 (excellent quality), according to the Global Quality Score (GQS) and compared the results.

Results: ChatGPT surgical choice was consistent with those of glaucoma specialists in 35/60 cases (58%), compared to 19/60 (32%) of Gemini (p = 0.0001). Gemini was not able to complete the task in 16 cases (27%). Trabeculectomy was the most frequent choice for both chatbots (53% and 50% for ChatGPT and Gemini, respectively). In "challenging" cases, ChatGPT agreed with specialists in 9/20 choices (45%), outperforming Google Gemini performances (4/20, 20%). Overall, GQS scores were 3.5 ± 1.2 and 2.1 ± 1.5 for ChatGPT and Gemini (p = 0.002). This difference was even more marked if focusing only on "challenging" cases (1.5 ± 1.4 vs. 3.0 ± 1.5, p = 0.001).

Conclusion: ChatGPT-4 showed a good analysis performance for glaucoma surgical cases, either ordinary or challenging. On the other side, Google Gemini showed strong limitations in this setting, presenting high rates of unprecise or missed answers.

Keywords: Artificial intelligence (AI); ChatGPT; Glaucoma; Glaucoma surgery; Google Bard; Google Gemini; Large language models (LLM).

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Screenshot of ChatGPT-4 responses in a “challenging” case. A, B Case description and ChatGPT answer analyzing the scenario and proposing several surgical treatments; C coherent answer when asked to choose only one among proposed treatments
Fig. 2
Fig. 2
Screenshot of Google Gemini responses in the same “challenging” case of Fig. 1. A Case description and Gemini analysis of the case; B when asked for surgical advice, Google Gemini provided more synthetic answers rich of web sources; C when asked to choose only one treatment, Gemini frequently answered “I can’t choose one treatment for this case.” However, it was able to present a list of surgical options, even though none of them was analyzed in details
Fig. 3
Fig. 3
Histograms showing A the level of agreement between ChatGPT and Google Gemini’s answers and those provided by glaucoma specialists in all cases and in “ordinary” and “challenging” scenarios. Complete agreement was assessed when the final choice of the chatbot was consistent with the one provided by specialists, while partial agreement included cases in which the correct answer was listed but not picked as preferred choice by the chatbot; B the comparison between the Global Quality Scores assigned by ophthalmologists to the two chatbots’ performance and usability (showed as mean and standard deviation). One asterisk (*) stands for statistical difference < 0.05; two asterisks (**) stand for p < 0.01
See this image and copyright information in PMC

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