Bowness, James S;
Liu, Xiaoxuan;
Keane, Pearse A;
(2024)
Leading in the development, standardised evaluation, and adoption of artificial intelligence in clinical practice: regional anaesthesia as an example.
British Journal of Anaesthesia
10.1016/j.bja.2023.12.024.
(In press).
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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.
| Type: | Article |
|---|---|
| Title: | Leading in the development, standardised evaluation, and adoption of artificial intelligence in clinical practice: regional anaesthesia as an example |
| Location: | England |
| DOI: | 10.1016/j.bja.2023.12.024 |
| Publisher version: | http://dx.doi.org/10.1016/j.bja.2023.12.024 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Artificial intelligence; regional anaesthesia; ultrasound; evaluation; medical devices; regulation; standardisation |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Institute of Ophthalmology |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10187352 |
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