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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

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]. …

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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 CrossRefPubMed

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 CrossRefPubMed

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 CrossRefPubMed

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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
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-023-10240-y

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