Learning the Randleman Criteria in Refractive Surgery: Utilizing ChatGPT-3.5 Versus Internet Search Engine

Abstract

Introduction Large language models such as OpenAI's (San Francisco, CA) ChatGPT-3.5 hold immense potential to augment self-directed learning in medicine, but concerns have risen regarding its accuracy in specialized fields. This study compares ChatGPT-3.5 with an internet search engine in their ability to define the Randleman criteria and its five parameters within a self-directed learning environment. Methods Twenty-three medical students gathered information on the Randleman criteria. Each student was allocated 10 minutes to interact with ChatGPT-3.5, followed by 10 minutes to search the internet independently. Each ChatGPT-3.5 conversation, student summary, and internet reference were subsequently analyzed for accuracy, efficiency, and reliability. Results ChatGPT-3.5 provided the correct definition for 26.1% of students (6/23, 95% CI: 12.3% to 46.8%), while an independent internet search resulted in sources containing the correct definition for 100% of students (23/23, 95% CI: 87.5% to 100%, p = 0.0001). ChatGPT-3.5 incorrectly identified the Randleman criteria as a corneal ectasia staging system for 17.4% of students (4/23), fabricated a "Randleman syndrome" for 4.3% of students (1/23), and gave no definition for 52.2% of students (12/23). When a definition was given (47.8%, 11/23), a median of two of the five correct parameters was provided along with a median of two additional falsified parameters. Conclusion Internet search engine outperformed ChatGPT-3.5 in providing accurate and reliable information on the Randleman criteria. ChatGPT-3.5 gave false information, required excessive prompting, and propagated misunderstandings. Learners should exercise discernment when using ChatGPT-3.5. Future initiatives should evaluate the implementation of prompt engineering and updated large-language models.

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