Can Large Language Models Help Healthcare?

Abstract

Large-language models (LLMs) are used in medicine.LLMs have 'natural language capabilities' and 'generative AI' capabilities.LLMs function as 'probabilistic models' that select the next appropriate word or phrase in response to user input, based on the context and content.This mechanism generates the most probable (i.e., contextually appropriate) response without any specific long-term goals or objectives.LLMs then learn large amounts of textual data, not to say that they are understanding, but that they acquire the ability to generate responses based on the training data.LLMs have shown the potential to automatically summarize information in electronic medical records, thereby reducing the burden on medical practices and allowing doctors to concentrate more on patient care 1) .ChatGPT is an LLMs.This study evaluated the accuracy and reproducibility of the ChatGPT-3.5(OpenAI) responses to clinical questions (CQs) regarding the Japanese Atherosclerosis Society's Guidelines for the Prevention of Atherosclerotic Cardiovascular Disease, 2022 Edition (JAS Guidelines 2022).The accuracy of background questions (BQs) and foreground questions (FQs) in clinical practice decision-making was independently assessed by three researchers using a six-point Likert scale.The results showed that the ChatGPT responses were highly accurate and reproducible, particularly for the FQs, and the FQs are speculated to be more precise than the BQs because they are based on evidence from randomized controlled trials 2, 3) .Kusunose et al. also evaluated the accuracy of ChatGPT responses to the CQs in the Japanese Society of Hypertension (JSH) 2019 guidelines and reported 4) .While these studies suggest that LLMs, such as ChatGPT, can effectively assist healthcare professionals in interpreting guidelines, some limitations must also

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