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

Bowness, James S
; 
Liu, Xiaoxuan
; 
Keane, Pearse A
Abstract
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.
MIDER Authors
Liu, Xiaoxian
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Date
2024-02-01
Type
Other
Subject
Anaesthesia
Collections
UHB Anesthetics
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Citation
Bowness JS, Liu X, Keane PA. Leading in the development, standardised evaluation, and adoption of artificial intelligence in clinical practice: regional anaesthesia as an example. Br J Anaesth. 2024 May;132(5):1016-1021. doi: 10.1016/j.bja.2023.12.024.
Journal / Source Title
British Journal of Anaesthesia
DOI
10.1016/j.bja.2023.12.024
URI
http://hdl.handle.net/20.500.14200/4960
PMID
38302346
Publisher
Elsevier
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