Guy Parsons
Impact in
- Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education
-
- Artificial Intelligence in Healthcare
Papers in
-
- Cardiac Imaging and Diagnostics 2
- COVID-19 diagnosis using AI 2
-
- Cardiac Arrest and Resuscitation 1
- Co-authors
- Adam Mahdi (6 shared papers)Bartłomiej W. Papież (3 shared papers)Lionel Tarassenko (3 shared papers)Jon S. Bailey (1 shared paper)Terry Lyons (2 shared papers)Oana-Maria Camburu (1 shared paper)Roxy Senior (3 shared papers)Ivan Kiskin (1 shared paper)
- Journals
- Information Fusion (1 paper)European Heart Journal - Cardiovascular Imaging (1 paper)Nature Medicine (1 paper)The American Journal of Medicine (1 paper)Lecture notes in computer science (3 papers)
- Partner nations
- United KingdomAustria
In The Last Decade
Guy Parsons
11 papers receiving 184 citations
Guy Parsons's Hit Papers
Peers
Comparison fields: 5 of 55
- Health Informatics 51
- Health Information Management 15
- Artificial Intelligence 44
- Emergency Medicine 10
- Computer Vision and Pattern Recognition 27
Countries citing papers authored by Guy Parsons
This map shows the geographic impact of Guy Parsons'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 Guy Parsons with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Guy Parsons more than expected).
Fields of papers citing papers by Guy Parsons
This network shows the impact of papers produced by Guy Parsons. 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 Guy Parsons. The network helps show where Guy Parsons may publish in the future.
Co-authors
The 13 scholars most cited alongside Guy Parsons, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Review of multimodal machine learning approaches in healthcare Hit paper breakdown → | 2024 | 101 |
| 2 | 2026 | 16 | |
| 3 | 2021 | 15 | |
| 4 | 2022 | 14 | |
| 5 | 2019 | 12 | |
| 6 | 2022 | 10 | |
| 7 | 2016 | 8 | |
| 8 | 2016 | 4 | |
| 9 | 2022 | 4 | |
| 10 | 2023 | 4 | |
| 11 | 2016 | 2 |
About Guy Parsons
Guy Parsons is a scholar working on Radiology, Nuclear Medicine and Imaging, Emergency Medicine, Health Information Management, Artificial Intelligence and Health Informatics, having authored 11 papers that have together received 190 indexed citations. Recurring topics across this work include Machine Learning in Healthcare (3 papers), Artificial Intelligence in Healthcare (2 papers), Cardiac Imaging and Diagnostics (2 papers), COVID-19 diagnosis using AI (2 papers), Takotsubo Cardiomyopathy and Associated Phenomena (1 paper), Phonocardiography and Auscultation Techniques (1 paper), Artificial Intelligence in Healthcare and Education (1 paper) and Cardiac Arrest and Resuscitation (1 paper). The work is most often cited by research in Health Informatics (51 citations), Health Information Management (15 citations), Artificial Intelligence (44 citations), Emergency Medicine (10 citations) and Computer Vision and Pattern Recognition (27 citations). Guy Parsons has collaborated with scholars based in United Kingdom and Austria. Frequent co-authors include Adam Mahdi, Bartłomiej W. Papież, Lionel Tarassenko, Jon S. Bailey, Terry Lyons, Oana-Maria Camburu, Roxy Senior, Ivan Kiskin, Thomas Lukasiewicz and Sothinathan Gurunathan. Their work appears in journals such as Information Fusion, European Heart Journal - Cardiovascular Imaging, Nature Medicine, The American Journal of Medicine and Lecture notes in computer science.
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.