Retrieve, Summarize, and Verify: How Will ChatGPT Affect Information Seeking from the Medical Literature?

作者：Qiao Jin, Robert Leaman, Zhiyong Lu · 发表于：Journal of the American Society of Nephrology · 年份：2023 · DOI：10.1681/asn.0000000000000166 · 被引用次数：71 · 研究领域：Artificial Intelligence in Healthcare and Education、Topic Modeling、Machine Learning in Healthcare

ChatGPT, a general purpose chatbot developed by OpenAI, has been widely reported to have the potential to revolutionize how people interact with information online.1 Like other large language models (LLMs), ChatGPT has been trained on a large text corpus to predict probable words from the surrounding context. ChatGPT, however, has received substantial popular attention for generating human-like conversational responses, and new developments are occurring rapidly. The initial release in November 2022 used the model generative pretrained transformers (GPT)-3.5, and a version based on GPT-4.0 was released in March 2023. Recent work has discussed applications of ChatGPT for medical education and clinical decision support.2–4 However, health care professionals should be aware of the drawbacks and limitations—and potential capabilities—of using ChatGPT and similar LLMs to interact with medical knowledge. A significant issue with ChatGPT is its tendency to generate confident-sounding but fabricated responses, commonly known as “hallucination.” Although it can generate plausible-sounding text, ChatGPT does not consult any source of truth.1 Occasional incorrect or biased responses are, therefore, inevitable, and ChatGPT cannot faithfully cite sources to allow their evidence to be verified. For example, we prompted GPT-3.5–based ChatGPT to list possible mechanisms of AKI in patients with coronavirus disease 2019 (COVID-19), with references. ChatGPT produced a list of plausible mechanis...