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Open Access 22-09-2023 | Artificial Intelligence | Commentary

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: Benjamin Bender

Published in: European Radiology | Issue 2/2024

Excerpt

The workload of radiologists has seen relentless growth in recent years [1]. While the increase of raw image data is not linearly linked to increased reading time, and there is no credible relationship between the speed of diagnostic image interpretation and accuracy [2], many argue that artificial intelligence (AI) could become a helpful tool to aid radiologists [3] or even eliminate the need for radiologists [4]. …

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

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.19.21290CrossRefPubMed

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.21.25564CrossRefPubMed

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.21.25484CrossRefPubMed

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/s00330-023-10074-8CrossRefPubMed

Miyakoshi A, Nguyen QT, Cohen WA, Talner LB, Anzai Y (2009) J Am Coll Radiol 6(12):864–870. https://doi.org/10.1016/j.jacr.2009.07.021CrossRefPubMed

Oren O, Gersh BJ, Bhatt DL (2020) Artificial intelligence in medical imaging: switching from radiographic pathological data to clinically meaningful endpoints. Lancet Digit Health 2(9):e486–e488. https://doi.org/10.1016/S2589-7500(20)30160-6CrossRefPubMed

Title: 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: Benjamin Bender
Publication date: 22-09-2023
Publisher: Springer Berlin Heidelberg
Keyword: Artificial Intelligence
Published in: European Radiology / Issue 2/2024
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-023-10240-y

2024 ASCO Annual Meeting

Springer Medicine

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

Excerpt

2024 ASCO Annual Meeting

Springer Medicine

Excerpt

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