What do medical students actually need to know about artificial intelligence?

被引：124

作者：

McCoy, Liam G. ^{[1
,2
]}

Nagaraj, Sujay ^{[1
,3
]}

Morgado, Felipe ^{[1
,4
]}

Harish, Vinyas ^{[1
,2
]}

Das, Sunit ^{[1
,5
]}

Celi, Leo Anthony ^{[6
,7
,8
]}

机构：

[1] Univ Toronto, Fac Med, Med Sci Bldg,1 Kings Coll Cir, Toronto, ON M5S 1A8, Canada

[2] Univ Toronto, Dalla Lana Sch Publ Hlth, Inst Hlth Policy Management & Evaluat, 155 Coll St 4th Floor, Toronto, ON M5T 3M6, Canada

[3] Univ Toronto, Dept Comp Sci, 40 St George St,Room 4283, Toronto, ON M5S 2E4, Canada

[4] Univ Toronto, Dept Med Biophys, 101 Coll St,Suite 15-701, Toronto, ON M5G 1L7, Canada

[5] Univ Toronto, Ctr Eth, 15 Devonshire Pl, Toronto, ON M5S 1H8, Canada

[6] MIT, Inst Med Engn & Sci, 77 Massachusetts Ave,E25-505, Cambridge, MA 02139 USA

[7] Beth Israel Deaconess Med Ctr, Div Pulm Crit Care & Sleep Med, 330 Brookline Ave, Boston, MA 02215 USA

[8] Harvard TH Chan Sch Publ Hlth, Dept Biostat, 677 Huntington Ave, Boston, MA 02115 USA

来源：

NPJ DIGITAL MEDICINE | 2020年 / 3卷 / 01期

关键词：

EDUCATION;

D O I：

10.1038/s41746-020-0294-7

中图分类号：

R19 [保健组织与事业（卫生事业管理）];

学科分类号：

摘要：

With emerging innovations in artificial intelligence (AI) poised to substantially impact medical practice, interest in training current and future physicians about the technology is growing. Alongside comes the question of what, precisely, should medical students be taught. While competencies for the clinical usage of AI are broadly similar to those for any other novel technology, there are qualitative differences of critical importance to concerns regarding explainability, health equity, and data security. Drawing on experiences at the University of Toronto Faculty of Medicine and MIT Critical Data's "datathons", the authors advocate for a dual-focused approach: combining robust data science-focused additions to baseline health research curricula and extracurricular programs to cultivate leadership in this space.

引用

页数：3

共 15 条

[1] A "datathon" model to support cross-disciplinary collaboration [J].