arrow
返回

Ten Ways Artificial Intelligence Will Transform Primary Care

delete2019-05-14
delete116
delete
OA
AI
S
Steven Lin *
M
Megan Mahoney
C
Christine A. Sinsky
DOI:10.1007/s11606-019-05035-1delete
delete原文链接
delete分享
delete收藏
查看原文
摘要

摘要

En 中文
Artificial intelligence (AI) is poised as a transformational force in healthcare. This paper presents a current environmental scan, through the eyes of primary care physicians, of the top ten ways AI will impact primary care and its key stakeholders. We discuss ten distinct problem spaces and the most promising AI innovations in each, estimating potential market sizes and the Quadruple Aims that are most likely to be affected. Primary care is where the power, opportunity, and future of AI are most likely to be realized in the broadest and most ambitious scale. We propose how these AI-powered innovations must augment, not subvert, the patient-physician relationship for physicians and patients to accept them. AI implemented poorly risks pushing humanity to the margins; done wisely, AI can free up physicians' cognitive and emotional space for patients, and shift the focus away from transactional tasks to personalized care. The challenge will be for humans to have the wisdom and willingness to discern AI's optimal role in twenty-first century healthcare, and to determine when it strengthens and when it undermines human healing. Ongoing research will determine the impact of AI technologies in achieving better care, better health, lower costs, and improved well-being of the workforce.
Keyword:
artificial intelligence
primary care
Quadruple Aim
patient-physician relationship
AI总结

AI总结

对已上传原文的论文进行重点信息的提取,主要内容包括:简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。

期刊

Journal of General Internal Medicine 封面图
Journal of General Internal Medicine
IF:
4.2
论文数:
1.3W
被引数:
3.2W

机构

S
Stanford University
学者数:
9.6W
论文数: 8.2W
被引数: 17.0W
American Medical Association 封面图
American Medical Association
学者数:
369
论文数: 383
被引数: 657
引用论文

引用论文

Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists
err2018-08-01
err557
errOAAI
errHaenssle, H. A.; Fink, C.; Schneiderbauer, R.; Toberer, F.; Buhl, T.; Blum, A.; Kalloo, A.; Hassens, A. Ben Hadj; Thomas, L.; Enk, A.; Uhlmann, L.
err分享
err收藏
Scalable and accurate deep learning with electronic health records通过电子健康记录进行可扩展且准确的深度学习
err2018-05-08
err1.4K
errOAAI
errRajkomar, Alvin; Oren, Eyal; Chen, Kai; Dai, Andrew M.; Hajaj, Nissan; Hardt, Michaela; Liu, Peter J.; Liu, Xiaobing; Marcus, Jake; Sun, Mimi; Sundberg, Patrik; Yee, Hector; Zhang, Kun; Zhang, Yi; Flores, Gerardo; Duggan, Gavin E.; Irvine, Jamie; Quoc Le; Litsch, Kurt; Mossin, Alexander; Tansuwan, Justin; Wang, De; Wexler, James; Wilson, Jimbo; Ludwig, Dana; Volchenboum, Samuel L.; Chou, Katherine; Pearson, Michael; Madabushi, Srinivasan; Shah, Nigam H.; Butte, Atul J.; Howell, Michael D.; Cui, Claire; Corrado, Greg S.; Dean, Jeffrey
err分享
err收藏
err分享
err收藏
err分享
err收藏
Reimagining Clinical Documentation With Artificial Intelligence
err2018-05-01
err45
PREAI
errLin, Steven Y.; Shanafelt, Tait D.; Asch, Steven M.
err分享
err收藏
err分享
err收藏
A Comparison of Artificial Intelligence and Human Doctors for the Purpose of Triage and Diagnosis人工智能和人类医生在分诊和诊断方面的比较
err2020-11-30
err71
errOAAI
errBaker, Adam; Perov, Yura; Middleton, Katherine; Baxter, Janie; Mullarkey, Daniel; Sangar, Davinder; Butt, Mobasher; DoRosario, Arnold; Johri, Saurabh
err分享
err收藏
学者 查看更多内容