Ready for testing artificial intelligence in radiology clinical practice: We would do well to be in the front line leveraging their strengths but also highlighting today weaknesses
Author:
Funder
Universitätsklinikum Tübingen
Publisher
Springer Science and Business Media LLC
Subject
Radiology, Nuclear Medicine and imaging,General Medicine
Link
https://link.springer.com/content/pdf/10.1007/s00330-023-10240-y.pdf
Reference7 articles.
1. Alexander R, Waite S, Bruno MA et al (2022) Mandating limits on workload, duty, and speed in radiology. Radiology 304(2):274–282. https://doi.org/10.1148/radiol.212631
2. Muroff LR, Berlin L (2019) Speed versus interpretation accuracy: current thoughts and literature review. AJR Am J Roentgenol 213(3):490–492. https://doi.org/10.2214/AJR.19.21290
3. Miles CR, Lehman CD (2021) Artificial intelligence for image interpretation: point – the radiologist’s potential friend. AJR Am J Roentgenol 217:556–557. https://doi.org/10.2214/AJR.21.25564
4. Lexa FJ, Jha S (2021) Artificial intelligence for image interpretation: counterpoint – the radiologist’s incremental foe. AJR Am J Roentgenol 217:558–559. https://doi.org/10.2214/AJR.21.25484
5. Buchlak QD, Tang CHM, Seah JCY et al (2023) Effects of a comprehensive brain computed tomography deep learning model on radiologist detection accuracy. Eur Radiol. https://doi.org/10.1007/s00330-023-10074-8
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