Devon-Sand Anna, Sayres Rory, Liu Yun, Strachan Patricia, Smith Margaret A, Nguyen Trinh, Ko Justin M, Lin Steven
Stanford School of Medicine, Stanford, CA.
Google Research, Google, Mountain View, CA.
Mayo Clin Proc Digit Health. 2024 Jul 31;2(3):463-469. doi: 10.1016/j.mcpdig.2024.07.001. eCollection 2024 Sep.
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.
人工智能（AI）技术有潜力扩大高质量健康信息和医疗服务的获取途径。了解不同用户与技术的交互方式有助于改进人工智能模型和用户界面，从而最大限度地提高其对更多人的潜在益处。本叙述描述了技术开发者、学术研究人员以及一个社区组织的代表如何合作开展一个以社区为中心的新兴健康技术项目。我们的项目团队由斯坦福医学中心、谷歌以及圣克拉拉家庭健康计划的布兰卡·阿尔瓦拉多社区资源中心的代表组成。我们旨在了解加利福尼亚州东圣何塞社区成员对一种人工智能驱动的皮肤科工具的可用性和可接受性。具体而言，我们的目标如下：测试基于人工智能的健康技术跨部门研究模型；确定该工具在不同种族和年龄人群中的效用；从社区活动招募的参与者那里获得深入的用户体验反馈；提供免费的皮肤健康咨询；并提供接受后续护理的资源。我们描述了一种各方贡献专业知识的协作方法：社区健康合作伙伴对社区的了解、学术研究机构的临床专业知识以及科技公司的软件和人工智能专业知识。通过迭代过程，我们确定了重要的社区需求，包括技术、语言和隐私支持。我们的方法使我们能够招募并吸引不同类型的参与者，其中超过70%的人更喜欢使用英语以外的语言。我们从规划和执行这个案例研究中提炼出经验教训，这些经验教训可能有助于其他合作者在人工智能医疗创新中弥合学术界、产业界和社区之间的差距。
