GPT-3 has been trained on several major data sources, such as Common Crawl, WebText2, Books1, Books2, and English-language Wikipedia. GPT-3 was designed as a language generation model that adapts to answer not only yes/no questions but also complex ones. In GPT-3, autoregressive language models predict the next element in a text, based on previous natural-language texts. It was trained using 175 billion parameters. It is an autoregressive language model, which is a type of statistical language model that predicts the probability of a word in a text sequence. Generative Pretrained Transformer-3 (GPT-3) is a third-generation generative pretrained transformer model. However, the order of these lists can be improved in the future. This suggests that AI chatbots such as ChatGPT-3 can generate a well-differentiated diagnosis list for common chief complaints. In summary, this study demonstrates the high diagnostic accuracy of differential-diagnosis lists generated by ChatGPT-3 for clinical cases with common chief complaints. The rate of consistent differential diagnoses among physicians within the ten differential-diagnosis lists generated by ChatGPT-3 was 62/88 (70.5%). The rate of correct diagnosis by physicians was also superior to that by ChatGPT-3 in the top diagnosis (53.3% vs. The rate of correct diagnosis by physicians was still superior to that by ChatGPT-3 within the five differential-diagnosis lists (98.3% vs. The rate of correct diagnosis by ChatGPT-3 within the ten differential-diagnosis lists was 28/30 (93.3%).
General internal medicine physicians created clinical cases, correct diagnoses, and five differential diagnoses for ten common chief complaints. This study evaluated the accuracy of differential-diagnosis lists generated by ChatGPT-3 for clinical vignettes with common chief complaints. The diagnostic accuracy of differential diagnoses generated by artificial intelligence (AI) chatbots, including the generative pretrained transformer 3 (GPT-3) chatbot (ChatGPT-3) is unknown.
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