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Title: Comparison of Gemini Advanced and ChatGPT 4.0's Performances on the Ophthalmology Resident Ophthalmic Knowledge Assessment Program (OKAP) Examination Review Question Banks.
Author: Gill GS, Tsai J, Moxam J, Sanghvi HA, Gupta S.
Journal: Cureus; 2024 Sep; 16(9):e69612. PubMed ID: 39421095.
Abstract:
Background With advancements in natural language processing, tools such as Chat Generative Pre-Trained Transformers (ChatGPT) version 4.0 and Google Bard's Gemini Advanced are being increasingly evaluated for their potential in various medical applications. The objective of this study was to systematically assess the performance of these language learning models (LLMs) on both image and non-image-based questions within the specialized field of Ophthalmology. We used a review question bank for the Ophthalmic Knowledge Assessment Program (OKAP) used by ophthalmology residents nationally to prepare for the Ophthalmology Board Exam to assess the accuracy and performance of ChatGPT and Gemini Advanced. Methodology A total of 260 randomly generated multiple-choice questions from the OphthoQuestions question bank were run through ChatGPT and Gemini Advanced. A simulated 260-question OKAP examination was created at random from the bank. Question-specific data were analyzed, including overall percent correct, subspecialty accuracy, whether the question was "high yield," difficulty (1-4), and question type (e.g., image, text). To compare the performance of ChatGPT-4 and Gemini on the difficulty of questions, we utilized the standard deviation of user answer choices to determine question difficulty. In this study, a statistical analysis of Google Sheets was conducted using two-tailed t-tests with unequal variance to compare the performance of ChatGPT-4.0 and Google's Gemini Advanced across various question types, subspecialties, and difficulty levels. Results In total, 259 of the 260 questions were included in the study as one question used a video that any form of ChatGPT could not interpret as of May 1, 2024. For text-only questions, ChatGPT-4.0.0 correctly answered 57.14% (148/259, p < 0.018), and Gemini Advanced correctly answered 46.72% (121/259, p < 0.018). Both versions answered most questions without a prompt and would have received a below-average score on the OKAP. Moreover, there were 27 questions utilizing a secondary prompt in ChatGPT-4.0 compared to 67 questions in Gemini Advanced. ChatGPT-4.0 performed 68.99% on easier questions (<2 on a scale from 1-4) and 44.96% on harder questions (>2 on a scale from 1-4). On the other hand, Gemini Advanced performed 49.61% on easier questions (<2 on a scale from 1-4) and 44.19% on harder questions (>2 on a scale from 1-4). There was a statistically significant difference in accuracy between ChatGPT-4.0 and Gemini Advanced for easy (p < 0.0015) but not for hard (p < 0.55) questions. For image-only questions, ChatGPT-4.0 correctly answered 39.58% (19/48, p < 0.013), and Gemini Advanced correctly answered 33.33% (16/48, p < 0.022), resulting in a statistically insignificant difference in accuracy between ChatGPT-4.0 and Gemini Advanced (p < 0.530). A comparison against text-only and image-based questions demonstrated a statistically significant difference in accuracy for both ChatGPT-4.0 (p < 0.013) and Gemini Advanced (p < 0.022). Conclusions This study provides evidence that ChatGPT-4.0 performs better on the OKAP-style exams and is improved compared to Gemini Advanced within the context of ophthalmic multiple-choice questions. This may show an opportunity for increased worth for ChatGPT in ophthalmic medical education. While showing promise within medical education, caution should be used as a more detailed evaluation of reliability is needed.

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