Related Work

Research & Publications

This page lists related work from the UTSW program that MAPLES and UT-REAL build on. Records are classified so UTSW studies are not presented as consortium outputs. Summaries are taken from publisher and preprint records, not from other pages on this site.

Published UTSW research

Peer-reviewed UTSW research describing the methods and deployments UT-REAL builds on. This includes work published before and after the multi-site award. These papers report UTSW studies rather than UT-REAL consortium outcomes.

Published Article in Press npj Digital Medicine · October 7, 2026

Prospective deployment of multimodal AI grading for medical student OSCEs

Huong-Tra Ngo, Ameer Hamza Shakur, Michael Holcomb, Shinyoung Kang, David Hein, Judah Gruen, Hunter Schuler, Philip Jarrett, Thomas Dalton, Krystle Campbell, Daniel Scott, Andrew R. Jamieson

npj Digital Medicine (2026). Published online October 7, 2026 (Article in Press).

Prospective deployment of MAPLES at UTSW for 222 medical students across written notes, audio, and video, with human review of low-scoring assessments and physician adjudication of selected AI–human disagreements.

UTSW prospective Fall 2025 MAPLES deployment that provides a foundation for UT-REAL multi-site work. This paper reports the UTSW deployment, not UT-REAL consortium outcomes.

DOI 10.1038/s41746-026-03352-4 Earlier Research Square preprint Collection: Transforming Medical Education through Artificial Intelligence
Published NEJM AI · November 27, 2024

Rubrics to Prompts: Assessing Medical Student Post-Encounter Notes with AI

Andrew R. Jamieson, Michael J. Holcomb, Thomas O. Dalton, Krystle K. Campbell, Sol Vedovato, Ameer Hamza Shakur, Shinyoung Kang, David Hein, Jack Lawson, Gaudenz Danuser, Daniel J. Scott

NEJM AI. 2024;1(12).

Peer-reviewed case study on using large language models to grade medical-student post-encounter notes from structured clinical rubrics.

Foundational UTSW research published before the UT-REAL multi-site award.

DOI 10.1056/AIcs2400631
Published JMIR AI · December 18, 2025

Physical Examination Identification in Medical Education Videos: Zero-Shot Multimodal AI With Temporal Sequence Optimization Study

Shinyoung Kang, Michael Holcomb, David Hein, Ameer Hamza Shakur, Thomas Dalton, Andrew Jamieson

JMIR AI. 2025;4:e76586.

Zero-shot multimodal models were used to locate physical-examination segments in 500 fifteen-minute OSCE videos from five UTSW stations. At 1-second sampling, the Crossref abstract reports GPT-4o recall of 0.998 (95% CI 0.994–1.000) and intersection-over-union of 0.784 (95% CI 0.765–0.803).

Foundational UTSW research published before the UT-REAL multi-site award.

DOI 10.2196/76586
Published Discover Artificial Intelligence · August 8, 2025

Applying state-of-the-art artificial intelligence to grading in simulation-based education: assessment, feedback, and ROI

Krystle K. Campbell, Michael J. Holcomb, Sol Vedovato, Lenora Young, Gaudenz Danuser, Thomas O. Dalton, Andrew R. Jamieson, Daniel J. Scott

Discov Artif Intell. 2025;5:202.

Peer-reviewed paper on applying current AI methods to grading in simulation-based education, covering assessment, feedback, and return on investment.

Foundational UTSW research published before the UT-REAL multi-site award.

DOI 10.1007/s44163-025-00417-3

Accepted for publication

Related UTSW research accepted by a peer-reviewed journal and awaiting publication. Links lead to the available preprints. These are not UT-REAL consortium outputs.

None posted yet.

Preprints and manuscripts under review

Public preprints and manuscripts under journal review. They have not completed peer review. They are related UTSW research, not UT-REAL consortium outputs.

Preprint medRxiv · Preprint posted January 18, 2026

Automated Assessment of OSCE Physical Exams using Multimodal AI

Shinyoung Kang, Michael J. Holcomb, Ameer H. Shakur, David Hein, Huong-Tra Ngo, Hunter Schuler, Philip C. Jarrett, Thomas O. Dalton, Andrew R. Jamieson

medRxiv 2026.01.09.26343786.

Retrospective ablation study of 300 video-recorded OSCE encounters across six stations. The preprint abstract reports quadratic weighted kappa of 0.830 (95% CI 0.773–0.880) for a synchronized three-camera native-video configuration, compared with 0.732 (95% CI 0.687–0.776) for standard human evaluators, both against a physician-adjudicated reference. This is a preprint and has not been peer reviewed.

UTSW retrospective study. Related research; not a UT-REAL consortium publication.

Preprint DOI 10.64898/2026.01.09.26343786

Presentations

Conference posters and talks from the Jamieson Lab program.

Published Symposium on Artificial Intelligence for Learning Health Systems (SAIL) 2026, Puerto Rico · May 2026

Deploying a Multimodal AI Platform for Automated OSCE Grading in Medical Education

Huong-Tra Ngo

SAIL 2026 poster. Presented by Tra Ngo.

Poster presented by Tra Ngo describing MAPLES as a multimodal platform for OSCE grading across written notes, communication, and physical examination. A walkthrough is on this site.

Jamieson Lab presentation of MAPLES deployment work. Not a UT-REAL consortium output.

View source Walkthrough on this site

UT-REAL consortium outputs

Research and presentations reporting the six-school UT-REAL award. Foundational UTSW papers are listed separately above.

None posted yet.

Questions about citing this work?

For permissions, collaboration, or a walkthrough of the underlying platform, contact the team. Partner-school faculty should start on Participating in UT-REAL.

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