Your browser doesn't support javascript.
loading
Large Language Models in Medicine: The Potentials and Pitfalls : A Narrative Review.
Omiye, Jesutofunmi A; Gui, Haiwen; Rezaei, Shawheen J; Zou, James; Daneshjou, Roxana.
Afiliação
  • Omiye JA; Department of Dermatology and Department of Biomedical Data Science, Stanford University, Stanford, California (J.A.O., R.D.).
  • Gui H; Department of Dermatology, Stanford University, Stanford, California (H.G., S.J.R.).
  • Rezaei SJ; Department of Dermatology, Stanford University, Stanford, California (H.G., S.J.R.).
  • Zou J; Department of Biomedical Data Science, Stanford University, Stanford, California (J.Z.).
  • Daneshjou R; Department of Dermatology and Department of Biomedical Data Science, Stanford University, Stanford, California (J.A.O., R.D.).
Ann Intern Med ; 177(2): 210-220, 2024 02.
Article em En | MEDLINE | ID: mdl-38285984
ABSTRACT
Large language models (LLMs) are artificial intelligence models trained on vast text data to generate humanlike outputs. They have been applied to various tasks in health care, ranging from answering medical examination questions to generating clinical reports. With increasing institutional partnerships between companies producing LLMs and health systems, the real-world clinical application of these models is nearing realization. As these models gain traction, health care practitioners must understand what LLMs are, their development, their current and potential applications, and the associated pitfalls in a medical setting. This review, coupled with a tutorial, provides a comprehensive yet accessible overview of these areas with the aim of familiarizing health care professionals with the rapidly changing landscape of LLMs in medicine. Furthermore, the authors highlight active research areas in the field that promise to improve LLMs' usability in health care contexts.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Medicina Limite: Humans Idioma: En Revista: Ann Intern Med Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Medicina Limite: Humans Idioma: En Revista: Ann Intern Med Ano de publicação: 2024 Tipo de documento: Article