Photo of Dr James S Bowness

Dr James S Bowness

Telephone:

Location:

  • University College Hospital Grafton Way Building

GMC/GDC number:

7016957

Professional background

Dr James S Bowness is a consultant in anaesthesia at University College Hospitals London NHS Foundation Trust and Honorary Associate Professor of Anaesthesia at University College London.

He went to medical school at the Universities of St Andrews and Cambridge, followed by postgraduate training in surgery and anaesthesia, critical care, perioperative medicine, and pain management. His current clinical practice focuses on ultrasound-guided regional anaesthesia.

Alongside NHS and university commitments, James works at GE Healthcare, a multinational medical technology company. He serves as a bridge between clinical, academic, and commercial entities to bring clinical insight and leadership into the development of AI-based medical devices.

At GEHC he works to develop AI-based medical devices in anaesthesia, critical care, peri-operative medicine, and point-of-case ultrasound.

Specialties

Research interests

James undertook a PhD at the University of Oxford, on AI-based ultrasound image interpretation for regional anaesthesia. He was appointed as an Honorary Associate Professor of Anaesthesia at UCL in 2024. His current research focuses on AI, medical devices, point-of-care ultrasound, and regional anaesthesia.

Publications

James has published widely in the fields of medical AI, medical devices, ultrasound/point-of-care ultrasound, and regional anaesthesia (peer-reviewed manuscripts, books, and book chapters). Selected publications (for full list, see Google Scholar and PubMed)

  • Delvaux BV, Maupain O, Giral T, Bowness JS, Mercadal L. Evaluation of AI-based nerve segmentation on ultrasound: are standard evaluation metrics relevant for the clinical setting? Br J Anaesth, 2024 (accepted – in press).
  • Lewis O, Lloyd J, Ferry J, et al (senior author). Regional anaesthesia research priorities: a Regional Anaesthesia UK (RA-UK) priority setting partnership involving patients, carers, and healthcare professionals. Anaesthesia, 2024 (published online ahead of print, doi: 10.1111/anae.16473).
  • Bowness JS, James K, Yarlett L, et al. Assistive artificial intelligence for enhanced patient access to ultrasound-guided regional anaesthesia. 2024, Br J Anaesth; 132: 1173 – 1175.
  • Bowness JS, Morse R, Lewis O, et al. Variability between human experts and artificial intelligence in identification of anatomical structures by ultrasound in regional anaesthesia: a framework for the evaluation of assistive artificial intelligence. Br J Anaesth, 2024; 132: 1063 – 1072.
  • Bowness JS, Metcalfe D, El-Boghdadly K, et al. Artificial intelligence for ultrasound scanning in regional anaesthesia: a scoping review of the evidence from multiple disciplines. Br J Anaesth, 2024; 132: 1049 – 1062.
  • Ferry J, Lewis O, Lloyd J, et al (senior author). Research priorities in regional anaesthesia: an international Delphi study. 2024, Br J Anaesth; 132: 1041 – 1048.
  • Bowness JS, Liu X, Keane P. Leading in the development, standardized evaluation, and adoption of AI in clinical practice: regional anaesthesia as an example. Br J Anaesth, 2024; 132: 1016 – 1021.
  • Bowness JS, Macfarlane AJR, Burckett-St Laurent D, et al. Evaluation of the impact of assistive artificial intelligence on ultrasound scanning for regional anaesthesia. Br J Anaesth, 2023; 130: 226 – 233.
  • Bowness JS, Burckett-St Laurent D, Hernandez N, et al. Assistive artificial intelligence for ultrasound image interpretation in regional anaesthesia: an external validation study. Br J Anaesth, 2023; 130: 217 – 225.
  • Ashken T, Bowness JS, Macfarlane AJR et al. Recommendations for anatomical structures to identify on ultrasound for the performance of intermediate and advanced blocks in ultrasound-guided regional anesthesia. Reg Anesth Pain Med, 2022; 47: 762 772.
  • Bowness JS, Pawa A, Turbitt L, et al. International consensus on anatomical structures to identify on ultrasound for the performance of basic blocks in ultrasound-guided regional anesthesia. Reg Anesth Pain Med, 2022; 47: 106 – 112.
  • Bowness JS, El-Boghdadly K, Woodworth G, et al. Exploring the utility of assistive artificial intelligence for ultrasound scanning in regional anesthesia. Reg Anesth Pain Med, 2022; 47: 375 – 379.
  • Bowness JS, Varsou O, Turbitt L, et al. Identifying anatomical structures on ultrasound: assistive artificial intelligence in ultrasound-guided regional anaesthesia. Clin Anat, 2021; 34: 802 – 809.
  • Bowness JS, El-Boghdadly K, Burckett-St Laurent D. Artificial intelligence for image interpretation in ultrasound-guided regional anaesthesia. Anaesthesia, 2021; 76: 602 – 607.