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Evaluating the performance of large language models: ChatGPT and Google Bard in generating differential diagnoses in clinicopathological conferences of neurodegenerative disorders
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Evaluating the performance of large language models: ChatGPT and Google Bard in generating differential diagnoses in clinicopathological conferences of neurodegenerative disorders
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Journal Article
Evaluating the performance of large language models: ChatGPT and Google Bard in generating differential diagnoses in clinicopathological conferences of neurodegenerative disorders
2024
Overview
This study explores the utility of the large language models (LLMs), specifically ChatGPT and Google Bard, in predicting neuropathologic diagnoses from clinical summaries. A total of 25 cases of neurodegenerative disorders presented at Mayo Clinic brain bank Clinico‐Pathological Conferences were analyzed. The LLMs provided multiple pathologic diagnoses and their rationales, which were compared with the final clinical diagnoses made by physicians. ChatGPT‐3.5, ChatGPT‐4, and Google Bard correctly made primary diagnoses in 32%, 52%, and 40% of cases, respectively, while correct diagnoses were included in 76%, 84%, and 76% of cases, respectively. These findings highlight the potential of artificial intelligence tools like ChatGPT in neuropathology, suggesting they may facilitate more comprehensive discussions in clinicopathological conferences. This study assessed the capability of large language models, namely ChatGPT and Google Bard, in predicting neuropathologic diagnoses from 25 cases presented at Mayo Clinic brain bank clinicopathological conferences. ChatGPT‐4 rendered correct diagnoses in 84% of cases, whereas ChatGPT‐3.5 and Google Bard each achieved 76%. These findings highlight the potential of large language models in neuropathology, suggesting they may facilitate more comprehensive discussions in clinicopathological conferences.
Publisher
John Wiley & Sons, Inc,John Wiley and Sons Inc
