Jesse Burk-Rafel

Assistant Professor

NYU School of Medicine

United States

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Jesse Burk-Rafel is an Assistant Professor at the NYU Grossman School of Medicine in the United States. His research focuses on the integration of artificial intelligence in medical education, examining its impact on clinical reasoning documentation and graduate clinical skills education. He has contributed to the discourse on community representation among physicians in residency programs, highlighting the importance of diversity in medical training. Additionally, his work explores the infrastructure required to leverage AI’s potential effectively in medical education.

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UNITED STATES

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Jesse Burk-Rafel

Research Interests

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Contact this professor

About

Articles (20)

How Data Analytics Can Be Leveraged to Enhance Graduate Clinical Skills Education

Large Language Model–Augmented Strategic Analysis of Innovation Projects in Graduate Medical Education

Large Language Model–Based Assessment of Clinical Reasoning Documentation in the Electronic Health Record Across Two Institutions: Development and Validation Study

Artificial intelligence based assessment of clinical reasoning documentation: an observational study of the impact of the clinical learning environment on resident documentation quality

Community Racial and Ethnic Representation among Physicians in US Internal Medicine Residency Programs

It Takes More Than Enthusiasm: The Missing Infrastructure to Unlock AI’s Potential in Medical Education

Macy Foundation Innovation Report Part II: From Hype to Reality: Innovators' Visions for Navigating AI Integration Challenges in Medical Education

Precision Medical Education: Institutional Strategies for Successful Implementation

A Theoretical Foundation to Inform the Implementation of Precision Education and Assessment

Characterizing Residents’ Clinical Experiences—A Step Toward Precision Education

Precision Medical Education.

A New Tool for Holistic Residency Application Review: Using Natural Language Processing of Applicant Experiences to Predict Interview Invitation.

Identifying Meaningful Patterns of Internal Medicine Clerkship Grading Distributions: Application of Data Science Techniques Across 135 U.S. Medical Schools.

The Undergraduate to Graduate Medical Education Transition as a Systems Problem: A Root Cause Analysis.

Development and Validation of a Machine Learning Model for Automated Assessment of Resident Clinical Reasoning Documentation.

Reimagining the Transition to Residency: A Trainee Call to Accelerated Action.

Development and Validation of a Machine Learning-Based Decision Support Tool for Residency Applicant Screening and Review.

The AMA Graduate Profile: Tracking Medical School Graduates Into Practice.

Systems-Level Reforms to the US Resident Selection Process: A Scoping Review.

Development of a Clinical Reasoning Documentation Assessment Tool for Resident and Fellow Admission Notes: a Shared Mental Model for Feedback.

Collaborators (6)

Abigail Winkel

Holly Caretta-Weyer

Tzu-Yu Wu

Brian Garibaldi

Brian Gin

Grant Lin

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