Innovative Study Reveals Performance of Large Language Models in Neurology Exams
A recent cross-sectional study examined the effectiveness of large language models (LLMs) in neurology board-style examinations. The study utilized a question bank approved by the American Board of Psychiatry and Neurology to gain insights into these advanced language models.
ChatGPT Version 2 Outperforms Predecessor and Humans
The study focused on two versions of the LLM ChatGPT: version 3.5 and version 4. The findings showed that LLM 2 significantly outperformed its predecessor and even surpassed the average human score on the neurology board examination. LLM 2 correctly answered 85.0% of questions, while the mean human score is 73.8%. This data suggests that large language models, with further improvements, could have significant applications in clinical neurology and healthcare.
ChatGPT Performs Better on Lower-Order Questions
Even the older model, LLM 1, demonstrated sufficient performance, scoring 66.8%, slightly below the human average. Both models consistently used confident language regardless of the correctness of their answers, indicating an area for improvement in future iterations. The study categorized questions into lower-order and higher-order based on the Bloom taxonomy. Both models performed better on lower-order questions, but LLM 2 excelled in both lower and higher-order questions, showcasing its versatility and cognitive abilities.
Hot Take: Implications for AI in Healthcare
The results of this innovative study highlight the potential of large language models like ChatGPT in the field of healthcare, particularly in clinical neurology. With their high accuracy in answering exam-style questions, these models could be valuable tools for medical professionals, providing quick and reliable information. However, further refinements are necessary to address areas of improvement, such as the use of confident language regardless of answer correctness. Overall, this study opens up exciting possibilities for the integration of AI technology in healthcare and medical education.
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