Privacy versus artificial intelligence in medicine

被引：0

作者：

Rohringer, Taryn J. ^{[1
]}

Budhkar, Akshay ^{[2
,5
]}

Rudzicz, Frank ^{[2
,3
,4
,5
]}

机构：

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

[2] Univ Toronto, Dept Comp Sci, 27 Kings Coll Circle, Toronto, ON M5S 3H7, Canada

[3] St Michaels Hosp, Li Ka Shing Knowledge Inst, 209 Victoria St, Toronto, ON M5B 1T8, Canada

[4] Surg Safety Technol, 250 Yonge St, Toronto, ON M5G 1B1, Canada

[5] Vector Inst Artificial Intelligence, 661 Univ,Suite 710, Toronto, ON M5G 1M1, Canada

来源：

UNIVERSITY OF TORONTO MEDICAL JOURNAL | 2019年 / 96卷 / 01期

关键词：

D O I：

暂无

中图分类号：

R5 [内科学];

学科分类号：

1002 ; 100201 ;

摘要：

As artificial intelligence is increasingly integrated into clinical practice, various crucial challenges will persist, especially with regards to data acquisition, reporting, and potential re-identification of patient data. This paper outlines these challenges and suggests some open questions and potential solutions. Given recent news of companies overstepping their bounds in the pursuit of patient data to train their systems, and new regulations around privacy of those data, this discussion is especially pertinent. Here, we suggest that a common good can be achieved in which data can be kept private while also useful for artificial intelligence in the practice of medicine.

引用

页码：51 / 53

页数：3

共 14 条

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