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Fostering a Healthy AI Ecosystem for Radiology: Conclusions of the 2018 RSNA Summit on AI in Radiology.为放射学培育健康的人工智能生态系统:2018年RSNA放射学人工智能峰会结论
Radiol Artif Intell. 2019 Mar 27;1(2):190021. doi: 10.1148/ryai.2019190021. eCollection 2019 Mar.
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Artificial intelligence to diagnose meniscus tears on MRI.人工智能诊断 MRI 半月板撕裂
Diagn Interv Imaging. 2019 Apr;100(4):243-249. doi: 10.1016/j.diii.2019.02.007. Epub 2019 Mar 28.
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The RSNA Pediatric Bone Age Machine Learning Challenge.RSNA 儿科骨龄机器学习挑战赛。
Radiology. 2019 Feb;290(2):498-503. doi: 10.1148/radiol.2018180736. Epub 2018 Nov 27.
4
Influence of Artificial Intelligence on Canadian Medical Students' Preference for Radiology Specialty: ANational Survey Study.人工智能对加拿大医学生放射科专业偏好的影响:一项全国调查研究。
Acad Radiol. 2019 Apr;26(4):566-577. doi: 10.1016/j.acra.2018.10.007. Epub 2018 Nov 11.
5
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Eur Radiol. 2019 Apr;29(4):1640-1646. doi: 10.1007/s00330-018-5601-1. Epub 2018 Jul 6.
6
Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology.加拿大放射学家协会关于放射学人工智能的白皮书。
Can Assoc Radiol J. 2018 May;69(2):120-135. doi: 10.1016/j.carj.2018.02.002. Epub 2018 Apr 11.
7
Deep Learning in Radiology.深度学习在放射学中的应用
Acad Radiol. 2018 Nov;25(11):1472-1480. doi: 10.1016/j.acra.2018.02.018. Epub 2018 Mar 30.
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Medical Specialty Choice and Related Factors of Brazilian Medical Students and Recent Doctors.巴西医学生和新晋医生的医学专业选择及相关因素
PLoS One. 2015 Jul 24;10(7):e0133585. doi: 10.1371/journal.pone.0133585. eCollection 2015.
9
The relationship between specialty choice and gender of U.S. medical students, 1990-2003.1990 - 2003年美国医学生的专业选择与性别的关系
Acad Med. 2005 Sep;80(9):797-802. doi: 10.1097/00001888-200509000-00003.

人工智能对圣保罗市医学生选择放射科作为专业的影响。

Impact of artificial intelligence on the choice of radiology as a specialty by medical students from the city of São Paulo.

作者信息

Brandes Gabriela Irene Garcia, D'Ippolito Giuseppe, Azzolini Anderson Gusatti, Meirelles Gustavo

机构信息

Escola Paulista de Medicina da Universidade Federal de São Paulo (EPM-Unifesp), São Paulo, SP, Brazil.

Grupo Fleury, São Paulo, SP, Brazil.

出版信息

Radiol Bras. 2020 May-Jun;53(3):167-170. doi: 10.1590/0100-3984.2019.0101.

DOI:10.1590/0100-3984.2019.0101
PMID:32587424
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7302904/
Abstract

OBJECTIVE

To evaluate the impact of artificial intelligence (AI) on undergraduate medical students' choice of radiology as a specialty.

MATERIALS AND METHODS

In February 2019, an anonymous online survey was sent to medical students. The research contemplated questions on how much students think they know about AI technologies, how much AI discourages them from choosing radiology as a specialty, and whether they believe there is a threat to the radiology job market.

RESULTS

A total of 101 students, most of them doing their internship, answered the questionnaire. More than half of them (52.5%) said they believe AI poses a threat to the radiology job market, but 64.3% claimed not to have proper knowledge about these new technologies, and 31.7% said they would like more information on the technologies' operation and progress before making a decision on whether or not to practice radiology as a specialty.

CONCLUSION

A significant proportion of the surveyed students perceive AI as a threat to the radiological practice, which impacts their career choice. However, the majority claims to have insufficient knowledge of it and believes more information is needed for decision-making.

摘要

目的

评估人工智能(AI)对本科医学生选择放射科作为专业的影响。

材料与方法

2019年2月,向医学生发送了一份匿名在线调查问卷。该研究考虑了关于学生认为自己对人工智能技术了解多少、人工智能在多大程度上阻碍他们选择放射科作为专业以及他们是否认为放射科就业市场存在威胁等问题。

结果

共有101名学生回答了问卷,其中大多数人正在实习。超过一半(52.5%)的人表示他们认为人工智能对放射科就业市场构成威胁,但64.3%的人声称对这些新技术没有足够的了解,31.7%的人表示在决定是否将放射科作为专业之前,他们希望获得更多关于技术操作和进展的信息。

结论

相当一部分接受调查的学生认为人工智能对放射科实践构成威胁,这影响了他们的职业选择。然而,大多数人声称对其了解不足,并认为需要更多信息来做出决策。