Brian C. Gin

12 papers receiving 311 citations

Peers

Brian C. Gin
Comparison fields: 5 of 72
  • Health Informatics 202
  • Family Practice 62
  • Computer Science Applications 22
  • Radiology, Nuclear Medicine and Imaging 82
  • Health Information Management 14
Replace Teresa Festl‐Wietek with:
Teresa Festl‐Wietek Germany
Carl Preiksaitis United States
Christian Rose United States
Yavuz Selim Kıyak Türkiye
M Healy Ireland
Priya S. Garg United States
Syed Latifi Canada
Robert Kaczmarczyk Germany
Hussein Uraiby United Kingdom
C. Donald Combs United States
Brian C. Gin relative to Teresa Festl‐Wietek Germany Teresa Festl‐Wietek's profile →
Citations per field
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Teresa Festl‐Wietek · 1×
Citations per year

Countries citing papers authored by Brian C. Gin

Since Specialization
Citations

This map shows the geographic impact of Brian C. Gin'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 Brian C. Gin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Brian C. Gin more than expected).

Fields of papers citing papers by Brian C. Gin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Brian C. Gin. 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 Brian C. Gin. The network helps show where Brian C. Gin may publish in the future.

Co-authors

The 19 scholars most cited alongside Brian C. Gin, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Brian C. Gin Line = papers co-authored together Brian C. Gin links everyone, so they are left out of the graph.

All Works

12 of 12 papers shown
#Work
1 2023197
2 202364
3 202119
4 20218
5 20258
6 20246
7 20246
8 20206
9 20233
10 20222
11 20251
12 20251

About Brian C. Gin

Brian C. Gin is a scholar working on Family Practice, Public Health, Environmental and Occupational Health, Health Informatics, Radiology, Nuclear Medicine and Imaging and General Health Professions, having authored 12 papers that have together received 321 indexed citations. Recurring topics across this work include Innovations in Medical Education (6 papers), Clinical Reasoning and Diagnostic Skills (6 papers), Artificial Intelligence in Healthcare and Education (5 papers), Simulation-Based Education in Healthcare (2 papers), Radiology practices and education (2 papers), Social and Intergroup Psychology (1 paper), Behavioral Health and Interventions (1 paper) and Mental Health Research Topics (1 paper). The work is most often cited by research in Health Informatics (202 citations), Family Practice (62 citations), Computer Science Applications (22 citations), Radiology, Nuclear Medicine and Imaging (82 citations) and Health Information Management (14 citations). Brian C. Gin has collaborated with scholars based in United States, Netherlands and Canada. Frequent co-authors include Christy Boscardin, Karen E. Hauer, Stefanie S. Sebok‐Syer, Martin G. Tolsgaard, Monica M. Cuddy, Mark D. Syer, Martin Pusic, Morten Bo Søndergaard Svendsen, Patricia O’Sullivan and Olle ten Cate. Their work appears in journals such as Academic Medicine, Medical Education, Advances in Health Sciences Education, Psychometrika and New England Journal of Medicine.

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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