The effect of using a large language model to respond to patient messages-Reference-Cited by-同舟云学术

The effect of using a large language model to respond to patient messages

Published:2024-06 Issue:6 Volume:6 Page:e379-e381
ISSN:2589-7500
Container-title:The Lancet Digital Health
language:en
Short-container-title:The Lancet Digital Health

Author:

Chen Shan,Guevara Marco,Moningi Shalini,Hoebers Frank,Elhalawani Hesham,Kann Benjamin H,Chipidza Fallon E,Leeman Jonathan,Aerts Hugo J W L,Miller Timothy,Savova Guergana K,Gallifant Jack,Celi Leo A,Mak Raymond H,Lustberg Maryam,Afshar Majid,Bitterman Danielle S

Funder

European Research Council

National Institutes of Health

Publisher

Elsevier BV

Reference9 articles.

1. Electronic health records and burnout: time spent on the electronic health record after hours and message volume associated with exhaustion but not with cynicism among primary care clinicians;Adler-Milstein;J Am Med Inform Assoc,2020

2. Trends in electronic health record inbox messaging during the COVID-19 pandemic in an ambulatory practice network in New England;Nath;JAMA Netw Open,2021

3. Use of artificial intelligence chatbots for cancer treatment information;Chen;JAMA Oncol,2023

4. Large language models encode clinical knowledge;Singhal;Nature,2023

5. Comparing physician and artificial intelligence chatbot responses to patient questions posted to a public social media forum;Ayers;JAMA Intern Med,2023

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