Brian C. Gin

12 papers receiving 380 citations

Brian C. Gin's Hit Papers

Educational Strategies for Clinical Supervision of Artificial Intelligence Use 2025 · 28 citations
280Years since publication510152025

Peers

Brian C. Gin
Comparison fields: 5 of 68
  • Health Informatics 218
  • Family Practice 33
  • Computer Science Applications 23
  • Health Information Management 10
  • Radiology, Nuclear Medicine and Imaging 46
Replace S. Ayhan Çalışkan with:
S. Ayhan Çalışkan Türkiye
M Healy Ireland
Kulamakan Kulasegaram Canada
Teresa Festl‐Wietek Germany
Argyro Kavadella Greece
Sanghee Yeo South Korea
Priya S. Garg United States
Syed Latifi Canada
Hussein Uraiby United Kingdom
Elena Wood United States
Brian C. Gin relative to S. Ayhan Çalışkan Türkiye S. Ayhan Çalışkan's profile →
Citations per field
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S. Ayhan Çalışkan · 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 21 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

14 of 14 papers shown
#Work
1 2023233
2 202373
3
Educational Strategies for Clinical Supervision of Artificial Intelligence Use
Hit paper breakdown →
202528
4 202121
5 20247
6 20206
7 20246
8 20255
9 20254
10 20233
11 20242
12 20222
13 20260
14 20180

About Brian C. Gin

Brian C. Gin is a scholar working on Health Informatics, Family Practice, Public Health, Environmental and Occupational Health, General Health Professions and Radiology, Nuclear Medicine and Imaging, having authored 14 papers that have together received 390 indexed citations. Recurring topics across this work include Artificial Intelligence in Healthcare and Education (6 papers), Clinical Reasoning and Diagnostic Skills (4 papers), Innovations in Medical Education (4 papers), Simulation-Based Education in Healthcare (1 paper), Behavioral Health and Interventions (1 paper), Foreign Body Medical Cases (1 paper), Ethics in medical practice (1 paper) and Mental Health Research Topics (1 paper). The work is most often cited by research in Health Informatics (218 citations), Family Practice (33 citations), Computer Science Applications (23 citations), Health Information Management (10 citations) and Radiology, Nuclear Medicine and Imaging (46 citations). Brian C. Gin has collaborated with scholars based in United States, Netherlands and Norway. Frequent co-authors include Christy Boscardin, Karen E. Hauer, Raja-Elie E. Abdulnour, Martin Pusic, Stefanie S. Sebok‐Syer, Monica M. Cuddy, Martin G. Tolsgaard, Mark D. Syer, Morten Bo Søndergaard Svendsen and Patricia O’Sullivan. Their work appears in journals such as Academic Medicine, Medical Education, Obstetrical & Gynecological Survey, Teaching and Learning in Medicine and Advances in Health Sciences Education.

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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