Large language models in medical and healthcare fields: applications, advances, and challenges

Abstract

Large language models (LLMs) are increasingly recognized for their advanced language capabilities, offering significant assistance in diverse areas like medical communication, patient data optimization, and surgical planning. Our survey meticulously searched for papers with keywords such as “medical,” “clinical,” “healthcare,” and “LLMs” across various databases, including ACM and Google Scholar. It sought to delve into the latest trends and applications of LLMs in healthcare, analyzing 175 relevant publications to support both practitioners and researchers in the field. We have compiled 56 experimental datasets, various evaluation methods and reviewed cutting-edge LLMs across tasks. Our comprehensive analysis of LLMs in healthcare applications, including medical question-answering, dialogue summarization, electronic health record generation, scientific research, medical education, medical product safety monitoring, clinical health reasoning, and clinical decision support. Furthermore, we have identified the challenges, including data security, inaccurate information, fairness and bias, plagiarism, copyrights, and accountability, and the potential solutions, namely de-identification framework, references,counterfactually fair prompting,opening and ending control codes, and establishing normative standards,to address these open issues,respectively. The findings of this survey exert a profound impact on spurring innovation in practical applications and addressing inherent challenges within the academic and medical communities.

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