This paper is in the following e-collection/theme issue:

Artificial Intelligence (AI) in Medical Education (209) Generative Language Models Including ChatGPT (760) e-Learning and Digital Medical Education (932) Natural Language Processing (937) Testing and Assessment in Medical Education (105) Design of Educational Technology (63) Chatbots and Conversational Agents (767) Theme Issue: ChatGPT and Generative Language Models in Medical Education (115)

Published on in Vol 10 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/56342, first published .
Assessing the Ability of a Large Language Model to Score Free-Text Medical Student Clinical Notes: Quantitative Study

Assessing the Ability of a Large Language Model to Score Free-Text Medical Student Clinical Notes: Quantitative Study

Assessing the Ability of a Large Language Model to Score Free-Text Medical Student Clinical Notes: Quantitative Study

Authors of this article:

Harry B Burke1 Author Orcid Image ;   Albert Hoang1 Author Orcid Image ;   Joseph O Lopreiato1 Author Orcid Image ;   Heidi King2 Author Orcid Image ;   Paul Hemmer1 Author Orcid Image ;   Michael Montgomery1 Author Orcid Image ;   Viktoria Gagarin1 Author Orcid Image
Article Authors Cited by (10) Tweetations (1) Metrics

Journals

  1. Jamieson A, Holcomb M, Dalton T, Campbell K, Vedovato S, Shakur A, Kang S, Hein D, Lawson J, Danuser G, Scott D. Rubrics to Prompts: Assessing Medical Student Post-Encounter Notes with AI. NEJM AI 2024;1(12) View
  2. Bany Abdelnabi A, Soykan B, Bhatti D, Rabadi G. Usefulness of Large Language Models (LLMs) for Student Feedback on H&P During Clerkship: Artificial Intelligence for Personalized Learning. ACM Transactions on Computing for Healthcare 2025 View
  3. Wei Y, Zhang R, Zhang J, Qi D, Cui W. Research on Intelligent Grading of Physics Problems Based on Large Language Models. Education Sciences 2025;15(2):116 View
  4. Kovari A. AI Gem: Context-Aware Transformer Agents as Digital Twin Tutors for Adaptive Learning. Computers 2025;14(9):367 View
  5. Chen J, Tu H, Chang C, Hsu W, Wang P, Liao C, Chen M. Automated Evaluation of Reflection and Feedback Quality in Workplace-Based Assessments by Using Natural Language Processing: Cross-Sectional Competency-Based Medical Education Study. JMIR Medical Education 2025;11:e81718 View
  6. Mitsuhashi M, Akiba Y, Saitou M, Suzuki K, Ogawa S, Masuno T. AI-Based Assessment of Non-Technical Skills in Prehospital Simulations: A Comparative Validation Study. Healthcare 2025;13(23):3121 View
  7. Takahashi H, Shikino K, Kondo T, Yamada Y, Tomoda Y, Kishi M, Aiyama Y, Nagai S, Enomoto A, Tokushima Y, Shinohara T, Sano F, Matsuura T, Watanabe R, Naito T. AI- Versus Human-Based Assessment of Medical Interview Transcripts in a Generative AI Simulated Patient System: Validation Study (Preprint). JMIR Medical Education 2025 View
  8. Basil M, Ahmed W, Hajeomar R, Strawbridge J, Lynch M, Mukhalalati B. A scoping review of the use of generative artificial intelligence tools in health profession education. BMC Medical Education 2026 View
  9. Hassanein F, Hussein R, Ahmed Y, El-Guindy J, Ahmed D, Abou-Bakr A. Calibration of AI large language models with human subject matter experts for grading of clinical short-answer responses in dental education. BMC Oral Health 2026 View

Books/Policy Documents

  1. Mihara H. Medical Education in the Age of Digital Transformation [Working Title]. View