James Bowness

842 total citations
40 papers, 366 citations indexed

About

James Bowness is a scholar working on Surgery, Cardiology and Cardiovascular Medicine and Critical Care and Intensive Care Medicine. According to data from OpenAlex, James Bowness has authored 40 papers receiving a total of 366 indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Surgery, 17 papers in Cardiology and Cardiovascular Medicine and 8 papers in Critical Care and Intensive Care Medicine. Recurrent topics in James Bowness's work include Cardiac, Anesthesia and Surgical Outcomes (17 papers), Surgical Simulation and Training (13 papers) and Anesthesia and Pain Management (10 papers). James Bowness is often cited by papers focused on Cardiac, Anesthesia and Surgical Outcomes (17 papers), Surgical Simulation and Training (13 papers) and Anesthesia and Pain Management (10 papers). James Bowness collaborates with scholars based in United Kingdom, United States and Denmark. James Bowness's co-authors include David Burckett-St Laurent, Kariem El‐Boghdadly, Alasdair Taylor, Helen Higham, J. Alison Noble, Lloyd Turbitt, Glenn E. Woodworth, Alan Macfarlane, David J. Phillips and Simeon J. West and has published in prestigious journals such as SHILAP Revista de lepidopterología, Critical Care and British Journal of Anaesthesia.

In The Last Decade

James Bowness

36 papers receiving 358 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
James Bowness United Kingdom 11 275 199 78 58 57 40 366
Maximilian Peter Forssten Sweden 12 305 1.1× 213 1.1× 13 0.2× 23 0.4× 5 0.1× 52 365
Hussain Alzayer Canada 4 123 0.4× 139 0.7× 3 0.0× 22 0.4× 60 1.1× 8 330
Alessandro Bellisario Italy 12 171 0.6× 296 1.5× 5 0.1× 64 1.1× 10 0.2× 31 464
Serdar Bozyel Türkiye 13 309 1.1× 426 2.1× 16 0.2× 2 0.0× 12 0.2× 46 512
Teemu Luostarinen Finland 12 62 0.2× 29 0.1× 23 0.3× 33 0.6× 7 0.1× 27 419
Jurij Matija Kališnik Slovenia 11 76 0.3× 223 1.1× 3 0.0× 23 0.4× 6 0.1× 39 296
Mathew Patteril United Kingdom 8 59 0.2× 65 0.3× 3 0.0× 24 0.4× 60 1.1× 15 249
Aída Luiza Ribeiro Turquetto Brazil 10 97 0.4× 117 0.6× 14 0.2× 11 0.2× 2 0.0× 25 256
Nilesh Sutaria United Kingdom 14 114 0.4× 380 1.9× 6 0.1× 16 0.3× 7 0.1× 32 487
Sullivan A. Ayuso United States 13 300 1.1× 48 0.2× 32 0.4× 5 0.1× 3 0.1× 53 384

Countries citing papers authored by James Bowness

Since Specialization
Citations

This map shows the geographic impact of James Bowness's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by James Bowness with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites James Bowness more than expected).

Fields of papers citing papers by James Bowness

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by James Bowness. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by James Bowness. The network helps show where James Bowness may publish in the future.

Co-authorship network of co-authors of James Bowness

This figure shows the co-authorship network connecting the top 25 collaborators of James Bowness. A scholar is included among the top collaborators of James Bowness based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with James Bowness. James Bowness is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Lloyd, James F., Alan Macfarlane, Jonathan Womack, et al.. (2024). Regional anaesthesia research priorities: a Regional Anaesthesia UK ( RA ‐ UK ) priority setting partnership involving patients, carers and healthcare professionals. Anaesthesia. 80(2). 170–178. 2 indexed citations
2.
Bowness, James, et al.. (2024). Accuracy of Non‐Invasive Imaging Techniques for the Diagnosis of MASH in Patients With MASLD : A Systematic Review. Liver International. 45(4). e16127–e16127. 3 indexed citations
3.
Bowness, James, David Metcalfe, Kariem El‐Boghdadly, et al.. (2024). Artificial intelligence for ultrasound scanning in regional anaesthesia: a scoping review of the evidence from multiple disciplines. British Journal of Anaesthesia. 132(5). 1049–1062. 16 indexed citations
4.
Macfarlane, Alan, David Burckett-St Laurent, Amit Pawa, et al.. (2024). Prospective randomized evaluation of the sustained impact of assistive artificial intelligence on anesthetists’ ultrasound scanning for regional anesthesia. SHILAP Revista de lepidopterología. 6(1). e000264–e000264. 2 indexed citations
5.
6.
Bowness, James, David Metcalfe, Kariem El‐Boghdadly, et al.. (2023). Artificial Intelligence for Anatomical Structure Identification on Ultrasound in Regional Anaesthesia: A Scoping Review Protocol. medRxiv. 1 indexed citations
7.
8.
James, Kathryn, et al.. (2023). AI-assisted ultrasound-guided regional anaesthesia for trauma patients: a service evaluation. British Journal of Anaesthesia. 131(3). e82–e82. 1 indexed citations
9.
10.
Bowness, James, Kariem El‐Boghdadly, Glenn E. Woodworth, et al.. (2022). Exploring the utility of assistive artificial intelligence for ultrasound scanning in regional anesthesia. Regional Anesthesia & Pain Medicine. 47(6). 375–379. 36 indexed citations
11.
Bowness, James, David Burckett-St Laurent, Nadia Hernandez, et al.. (2022). Assistive artificial intelligence for ultrasound image interpretation in regional anaesthesia: an external validation study. British Journal of Anaesthesia. 130(2). 217–225. 45 indexed citations
12.
Evans, Anne M., et al.. (2022). Pilot study to explore if an augmented reality NeedleTrainer device improves novice performance of a nerve block on a phantom. British Journal of Anaesthesia. 129(4). e109–e110. 1 indexed citations
13.
Bowness, James, Alan Macfarlane, David Burckett-St Laurent, et al.. (2022). Evaluation of the impact of assistive artificial intelligence on ultrasound scanning for regional anaesthesia. British Journal of Anaesthesia. 130(2). 226–233. 33 indexed citations
14.
Lloyd, James F., R. Morse, Alasdair Taylor, et al.. (2022). Artificial Intelligence: Innovation to Assist in the Identification of Sono-anatomy for Ultrasound-Guided Regional Anaesthesia. Advances in experimental medicine and biology. 1356. 117–140. 14 indexed citations
15.
Μoka, Εleni, et al.. (2021). Artificial Intelligence and Robotics in Regional Anaesthesia: Do they have a role?. Signa Vitae. 17(S1). 47–47. 3 indexed citations
16.
Bowness, James, Kariem El‐Boghdadly, & David Burckett-St Laurent. (2020). Artificial intelligence for image interpretation in ultrasound‐guided regional anaesthesia. Anaesthesia. 76(5). 602–607. 51 indexed citations
17.
Bowness, James & Alasdair Taylor. (2020). Ultrasound-Guided Regional Anaesthesia: Visualising the Nerve and Needle. Advances in experimental medicine and biology. 1235. 19–34. 19 indexed citations
18.
19.
Bowness, James, Kathy Nicholls, John D. Ferris, et al.. (2015). Finding the fifth intercostal space for chest drain insertion: guidelines and ultrasound. Emergency Medicine Journal. 32(12). 951–954. 12 indexed citations
20.
Bowness, James, et al.. (2014). Guidelines for chest drain insertion may not prevent damage to abdominal viscera. Emergency Medicine Journal. 32(8). 620–625. 10 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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