Leading in the development, standardised evaluation, and

Br J Anaesth · May 2024

Editorial

Leading in the development, standardised evaluation, and adoption of artificial intelligence in clinical practice: regional anaesthesia as an example.

A recent study by Suissa and colleagues explored the clinical relevance of a medical image segmentation metric (Dice metric) commonly used in the field of artificial intelligence (AI). They showed that pixel-wise agreement for physician identification of structures on ultrasound images is variable, and a relatively low Dice metric (0.34) correlated to a substantial agreement on subjective clinical assessment. We highlight the need to bring structure and clinical perspective to the evaluation of medical AI, which clinicians are best placed to direct.

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- James S Bowness, Xiaoxuan Liu, and Pearse A Keane.
- Nuffield Department of Clinical Anaesthesia, University of Oxford, Oxford, UK; Department of Anaesthesia, Aneurin Bevan University Health Board, Newport, UK. Electronic address: james.bowness@jesus.ox.ac.uk.
- Br J Anaesth. 2024 May 1; 132 (5): 101610211016-1021.
AbstractA recent study by Suissa and colleagues explored the clinical relevance of a medical image segmentation metric (Dice metric) commonly used in the field of artificial intelligence (AI). They showed that pixel-wise agreement for physician identification of structures on ultrasound images is variable, and a relatively low Dice metric (0.34) correlated to a substantial agreement on subjective clinical assessment. We highlight the need to bring structure and clinical perspective to the evaluation of medical AI, which clinicians are best placed to direct.Copyright © 2024 British Journal of Anaesthesia. Published by Elsevier Ltd. All rights reserved.

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Leading in the development, standardised evaluation, and adoption of artificial intelligence in clinical practice: regional anaesthesia as an example.

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