Tsai Timothy, Lee Julie J, Phillips Robert, Lin Steven
Stanford Healthcare AI Applied Research Team, Division of Primary Care and Population Health, Department of Medicine, Stanford University School of Medicine, Stanford, California
Stanford Healthcare AI Applied Research Team, Division of Primary Care and Population Health, Department of Medicine, Stanford University School of Medicine, Stanford, California.
Ann Fam Med. 2025 Jul 28;23(4):363-367. doi: 10.1370/afm.240459.
Artificial intelligence and machine learning (AI/ML) in health care is accelerating at a breathtaking pace. As the largest health care delivery platform, primary care is where the power, opportunity, and future of AI/ML are most likely to be realized in the broadest and most ambitious scale. However, there is a relative lack of organized, open, large-scale primary care datasets to attract industry and academia in primary care-focused research and development. This article proposes a set of high-level considerations around the data transformation that is needed to enable the growth of AI/ML applications in primary care. These considerations call for automation of data collection, organization of fragmented data, identification of primary care-specific use cases, integration of AI/ML into human workflows, and surveillance for unintended consequences. By unlocking the power of its data, primary care can play a leading role in advancing health care AI/ML to support patients, clinicians, and the health of the nation.
医疗保健领域的人工智能和机器学习（AI/ML）正在以惊人的速度加速发展。作为最大的医疗保健服务平台，初级保健最有可能在最广泛、最宏大的规模上实现AI/ML的力量、机遇和未来。然而，相对缺乏有组织、开放的大规模初级保健数据集，难以吸引行业和学术界开展以初级保健为重点的研发工作。本文围绕数据转换提出了一系列高层次的考量因素，这些因素是实现初级保健中AI/ML应用增长所必需的。这些考量因素要求实现数据收集自动化、整理碎片化数据、识别初级保健特定用例、将AI/ML集成到人工工作流程中，以及监测意外后果。通过释放其数据的力量，初级保健可以在推进医疗保健AI/ML以支持患者、临床医生和国家健康方面发挥主导作用。
