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

James S Bowness; Xiaoxuan Liu; Pearse A Keane

doi:10.1016/j.bja.2023.12.024

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. Epub 2024 Feb 1.

Authors

James S Bowness¹, Xiaoxuan Liu², Pearse A Keane³

Affiliations

¹ Nuffield Department of Clinical Anaesthesia, University of Oxford, Oxford, UK; Department of Anaesthesia, Aneurin Bevan University Health Board, Newport, UK. Electronic address: [email protected].
² University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK; College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK.
³ Institute of Ophthalmology, Faculty of Brain Sciences, University College London, London, UK; NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, UK.

PMID: 38302346
DOI: 10.1016/j.bja.2023.12.024

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.

Keywords: artificial intelligence; evaluation; medical devices; regional anaesthesia; regulation; standardisation; ultrasound.

Publication types

Editorial

MeSH terms

Anesthesia, Conduction*
Artificial Intelligence
Humans
Physicians*

Grants and funding

MR/T019050/1/MRC_/Medical Research Council/United Kingdom