Artificial intelligence in regional anesthesia
Emerging AI applications in UGRA warrant further study due to an opaque and fragmented evidence base. Robust and consistent evaluation and reporting of algorithm performance, in a representative clinical context, will expedite discovery and appropriate deployment of AI in UGRA. A clinician-focused approach to the development, evaluation, and implementation of this exciting branch of AI has huge potential to advance the human art of regional anesthesia.
Authors
- Joseph Harris (ORCID: https://orcid.org/0000-0003-2353-6847)
- Damon Kamming
- James Simeon Bowness (ORCID: https://orcid.org/0000-0002-8665-1984)
Institutions
- University College London Hospitals NHS Foundation Trust (GB)
- University College London (GB)
Publication Details
- Journal
- Current Opinion in Anaesthesiology
- Published
- 2025-04-21
- DOI
- https://doi.org/10.1097/aco.0000000000001505
- Citations
- 7
- Primary Topic
- Cardiac, Anesthesia and Surgical Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 9.72