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A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncology.

作者信息

Scheetz Jane, Rothschild Philip, McGuinness Myra, Hadoux Xavier, Soyer H Peter, Janda Monika, Condon James J J, Oakden-Rayner Luke, Palmer Lyle J, Keel Stuart, van Wijngaarden Peter

机构信息

Level 7, Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, 32 Gisborne Street, Melbourne, VIC, 3002, Australia.

Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Australia.

出版信息

Sci Rep. 2021 Mar 4;11(1):5193. doi: 10.1038/s41598-021-84698-5.


DOI:10.1038/s41598-021-84698-5
PMID:33664367
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7933437/
Abstract

Artificial intelligence technology has advanced rapidly in recent years and has the potential to improve healthcare outcomes. However, technology uptake will be largely driven by clinicians, and there is a paucity of data regarding the attitude that clinicians have to this new technology. In June-August 2019 we conducted an online survey of fellows and trainees of three specialty colleges (ophthalmology, radiology/radiation oncology, dermatology) in Australia and New Zealand on artificial intelligence. There were 632 complete responses (n = 305, 230, and 97, respectively), equating to a response rate of 20.4%, 5.1%, and 13.2% for the above colleges, respectively. The majority (n = 449, 71.0%) believed artificial intelligence would improve their field of medicine, and that medical workforce needs would be impacted by the technology within the next decade (n = 542, 85.8%). Improved disease screening and streamlining of monotonous tasks were identified as key benefits of artificial intelligence. The divestment of healthcare to technology companies and medical liability implications were the greatest concerns. Education was identified as a priority to prepare clinicians for the implementation of artificial intelligence in healthcare. This survey highlights parallels between the perceptions of different clinician groups in Australia and New Zealand about artificial intelligence in medicine. Artificial intelligence was recognized as valuable technology that will have wide-ranging impacts on healthcare.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4354/7933437/574025d287d8/41598_2021_84698_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4354/7933437/bd78f9c5c7a4/41598_2021_84698_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4354/7933437/acb256829087/41598_2021_84698_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4354/7933437/7c7dc7d0f04f/41598_2021_84698_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4354/7933437/42b93af46fea/41598_2021_84698_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4354/7933437/57d1cf3b729d/41598_2021_84698_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4354/7933437/999d764361c8/41598_2021_84698_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4354/7933437/45df120aedfe/41598_2021_84698_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4354/7933437/574025d287d8/41598_2021_84698_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4354/7933437/bd78f9c5c7a4/41598_2021_84698_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4354/7933437/acb256829087/41598_2021_84698_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4354/7933437/7c7dc7d0f04f/41598_2021_84698_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4354/7933437/42b93af46fea/41598_2021_84698_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4354/7933437/57d1cf3b729d/41598_2021_84698_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4354/7933437/999d764361c8/41598_2021_84698_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4354/7933437/45df120aedfe/41598_2021_84698_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4354/7933437/574025d287d8/41598_2021_84698_Fig8_HTML.jpg

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本文引用的文献

[1]
Medical robotics-Regulatory, ethical, and legal considerations for increasing levels of autonomy.

Sci Robot. 2017-3-15

[2]
Attitudes and perceptions of UK medical students towards artificial intelligence and radiology: a multicentre survey.

Insights Imaging. 2020-2-5

[3]
Artificial intelligence and the future of psychiatry: Insights from a global physician survey.

Artif Intell Med. 2019-11-18

[4]
Man against machine reloaded: performance of a market-approved convolutional neural network in classifying a broad spectrum of skin lesions in comparison with 96 dermatologists working under less artificial conditions.

Ann Oncol. 2020-1

[5]
A survey on the future of radiology among radiologists, medical students and surgeons: Students and surgeons tend to be more skeptical about artificial intelligence and radiologists may fear that other disciplines take over.

Eur J Radiol. 2019-11-9

[6]
Impact of artificial intelligence on radiology: a EuroAIM survey among members of the European Society of Radiology.

Insights Imaging. 2019-10-31

[7]
Physician perspectives on integration of artificial intelligence into diagnostic pathology.

NPJ Digit Med. 2019-4-26

[8]
Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices.

NPJ Digit Med. 2018-8-28

[9]
Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study.

Lancet Oncol. 2019-6-12

[10]
Impact of the rise of artificial intelligence in radiology: What do radiologists think?

Diagn Interv Imaging. 2019-5-6

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