Anna Devon-Sand MPH , Rory Sayres PhD , Yun Liu PhD , Patricia Strachan MSc , Margaret A. Smith MBA , Trinh Nguyen MA , Justin M. Ko MD , Steven Lin MD
{"title":"A Multiparty Collaboration to Engage Diverse Populations in Community-Centered Artificial Intelligence Research","authors":"Anna Devon-Sand MPH , Rory Sayres PhD , Yun Liu PhD , Patricia Strachan MSc , Margaret A. Smith MBA , Trinh Nguyen MA , Justin M. Ko MD , Steven Lin MD","doi":"10.1016/j.mcpdig.2024.07.001","DOIUrl":null,"url":null,"abstract":"<div><p>Artificial intelligence (AI)-enabled technology has the potential to expand access to high-quality health information and health care services. Learning how diverse users interact with technology enables improvements to the AI model and the user interface, maximizing its potential benefit for a greater number of people. This narrative describes how technology developers, academic researchers, and representatives from a community-based organization collaborated to conduct a community-centered project on emerging health technologies. Our project team comprised representatives from Stanford Medicine, Google, and Santa Clara Family Health Plan’s Blanca Alvarado Community Resource Center. We aimed to understand the usability and acceptability of an AI-driven dermatology tool among East San Jose, California, community members. Specifically, our objectives were as follows: to test a model for cross-sector research of AI-based health technology; to determine the utility of the tool in an ethnically and age-diverse population; to obtain in-depth user experience feedback from participants recruited during community events; to offer free skin health consultations; and to provide resources for receiving follow-up care. We describe a collaborative approach in which each party contributed expertise: knowledge of the community from the community health partner, clinical expertise from the academic research institution, and software and AI expertise from the technology company. Through an iterative process, we identified important community needs, including technological, language, and privacy support. Our approach allowed us to recruit and engage a diverse cohort of participants, over 70% of whom preferred a language other than English. We distill learnings from planning and executing this case study that may help other collaborators bridge the gap between academia, industry, and community in AI health care innovation.</p></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 3","pages":"Pages 463-469"},"PeriodicalIF":0.0000,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000713/pdfft?md5=7070082b704aa5765c6681bfe1a2ee2d&pid=1-s2.0-S2949761224000713-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mayo Clinic Proceedings. Digital health","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2949761224000713","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Artificial intelligence (AI)-enabled technology has the potential to expand access to high-quality health information and health care services. Learning how diverse users interact with technology enables improvements to the AI model and the user interface, maximizing its potential benefit for a greater number of people. This narrative describes how technology developers, academic researchers, and representatives from a community-based organization collaborated to conduct a community-centered project on emerging health technologies. Our project team comprised representatives from Stanford Medicine, Google, and Santa Clara Family Health Plan’s Blanca Alvarado Community Resource Center. We aimed to understand the usability and acceptability of an AI-driven dermatology tool among East San Jose, California, community members. Specifically, our objectives were as follows: to test a model for cross-sector research of AI-based health technology; to determine the utility of the tool in an ethnically and age-diverse population; to obtain in-depth user experience feedback from participants recruited during community events; to offer free skin health consultations; and to provide resources for receiving follow-up care. We describe a collaborative approach in which each party contributed expertise: knowledge of the community from the community health partner, clinical expertise from the academic research institution, and software and AI expertise from the technology company. Through an iterative process, we identified important community needs, including technological, language, and privacy support. Our approach allowed us to recruit and engage a diverse cohort of participants, over 70% of whom preferred a language other than English. We distill learnings from planning and executing this case study that may help other collaborators bridge the gap between academia, industry, and community in AI health care innovation.