Sonographer interaction with artificial intelligence: collaboration or conflict?-Reference-Cited by-同舟云学术

Sonographer interaction with artificial intelligence: collaboration or conflict?

Published:2023-07-31 Issue:2 Volume:62 Page:167-174
ISSN:0960-7692
Container-title:Ultrasound in Obstetrics & Gynecology
language:en
Short-container-title:Ultrasound in Obstet & Gyne

Author:

Day T. G.¹²^ORCID,Matthew J.²^ORCID,Budd S.²,Hajnal J. V.²,Simpson J. M.¹²,Razavi R.¹²,Kainz B.²³⁴

Affiliation:

1. Department of Congenital Cardiology, Evelina London Children's Healthcare Guy's and St Thomas' NHS Foundation Trust London UK

2. Faculty of Life Sciences and Medicine School of Biomedical Engineering and Imaging Sciences, King's College London London UK

3. Department of Artificial Intelligence in Biomedical Engineering Friedrich‐Alexander‐Universität Erlangen‐Nürnberg Erlangen Germany

4. Department of Computing, Faculty of Engineering Imperial College London London UK

Funder

National Institute for Health and Care Research

Wellcome Trust

Publisher

Wiley

Subject

Obstetrics and Gynecology,Radiology, Nuclear Medicine and imaging,Reproductive Medicine,General Medicine,Radiological and Ultrasound Technology

Link

https://obgyn.onlinelibrary.wiley.com/doi/pdf/10.1002/uog.26238

Reference54 articles.

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2. Deep learning

3. US Food and Drug Administration.Artificial intelligence and machine learning (AI/ML)‐enabled medical devices.https://www.fda.gov/medical‐devices/software‐medical‐device‐samd/artificial‐intelligence‐and‐machine‐learning‐aiml‐enabled‐medical‐devices[Accessed 16 December 2022].

4. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis

5. Artificial intelligence, fetal echocardiography, and congenital heart disease

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