%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 25
%N 
%P e48498
%T Toward Community-Based Natural Language Processing (CBNLP): Cocreating With Communities
%A Pillai,Malvika
%A Griffin,Ashley C
%A Kronk,Clair A
%A McCall,Terika
%+ Center for Biomedical Informatics Research, Stanford University School of Medicine, 1265 Welch Rd, Stanford, CA, 94305, United States, 1 650 724 3979, mpillai@stanford.edu
%K ChatGPT
%K natural language processing
%K community-based participatory research
%K research design
%K artificial intelligence
%K participatory
%K co-design
%K machine learning
%K co-creation
%K community based
%K lived experience
%K lived experiences
%K collaboration
%K collaborative
%D 2023
%7 4.8.2023
%9 Viewpoint
%J J Med Internet Res
%G English
%X Rapid development and adoption of natural language processing (NLP) techniques has led to a multitude of exciting and innovative societal and health care applications. These advancements have also generated concerns around perpetuation of historical injustices and that these tools lack cultural considerations. While traditional health care NLP techniques typically include clinical subject matter experts to extract health information or aid in interpretation, few NLP tools involve community stakeholders with lived experiences. In this perspective paper, we draw upon the field of community-based participatory research, which gathers input from community members for development of public health interventions, to identify and examine ways to equitably involve communities in developing health care NLP tools. To realize the potential of community-based NLP (CBNLP), research and development teams must thoughtfully consider mechanisms and resources needed to effectively collaborate with community members for maximal societal and ethical impact of NLP-based tools.
%M 37540551
%R 10.2196/48498
%U https://www.jmir.org/2023/1/e48498
%U https://doi.org/10.2196/48498
%U http://www.ncbi.nlm.nih.gov/pubmed/37540551