Brian Gin

Biographic Data

ID9555752
NAMEBrian Gin
GIVEN NAMESBrian
FAMILY NAMEGin
SIGNATUREGIN B
AFFILIATIONSDepartment of Pediatrics University of California San Francisco San Francisco California USA
ORCID0000-0001-7655-3750
VERIFIEDYes
TOTAL WORKS6
TOTAL CITATIONS0
AUTHOR COUNT6
EDITOR COUNT0
FIRST PUBLICATION YEAR2022
LATEST PUBLICATION YEAR2026
H-INDEX0

  • Twelve tips for data extraction for knowledge syntheses

    Lauren A Maggio, Joseph A Costello et al.ARTICLEMedical Teacher2026

    In medical education, the number of knowledge syntheses has increased dramatically, reflecting their growth and influence on education practice, research, and policy. However, despite the availability of instruction on many of the steps of conducting knowledge syntheses, practical guidance for the critical step of data extraction is limited. Data extraction is the process of systematically identifying and collecting information from the studies i…

  • Macy Foundation Innovation Report Part II

    Brian Gin, Kate Laforge et al.ARTICLEAcademic Medicine2025

  • Entrustment and EPAs for Artificial Intelligence (AI)

    Open AccessBrian Gin, Patricia O’sullivan et al.ARTICLEAcademic Medicine2025

    In this article, the authors propose a repurposing of the concept of entrustment to help guide the use of artificial intelligence (AI) in health professions education (HPE). Entrustment can help identify and mitigate the risks of incorporating generative AI tools with limited transparency about their accuracy, source material, and disclosure of bias into HPE practice. With AI’s growing role in education-related activities, like automated medical …

  • Evolving natural language processing towards a subjectivist inductive paradigm

    Open AccessBrian GinARTICLEMedical Education2023

    Gin reflects on the advantages of developing new natural language processing strategies that invite both researchers and their AI “assistants” into a collaborative and transparent process of inquiry

  • The fundamentals of Artificial Intelligence in medical education research

    Martin G Tolsgaard, Martin Pusic et al.ARTICLEMedical Teacher2023

    The use of Artificial Intelligence (AI) in medical education has the potential to facilitate complicated tasks and improve efficiency. For example, AI could help automate assessment of written responses, or provide feedback on medical image interpretations with excellent reliability. While applications of AI in learning, instruction, and assessment are growing, further exploration is still required. There exist few conceptual or methodological gu…

  • Exploring how feedback reflects entrustment decisions using artificial intelligence

    Open AccessBrian Gin, Olle ten Cate et al.ARTICLEMedical Education2022