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Funded by the UT REAL Health AI Pilot Program · University of Texas System
Scaling AI-Enabled
OSCE Assessment
Across Six UT Medical Schools
The MAPLES AI grading platform is being prepared for governed multi-site validation across UT System medical schools, funded by the UT REAL Health AI Pilot Program.
Explore MAPLES with sample materials. Demo access is by invitation; choose “Request access” on the sign-in page to get started.
LIVE
UTSW Production
6
UT Medical Schools
7,000+
UTSW Encounters Graded
Participating Medical Schools
Partnering across the UT System to validate AI-enabled clinical assessment.
Our purpose
Bringing the focus to teaching, learning, and discovery.
Our mission is to raise the quality and expand the possibilities of medical education by empowering educators, researchers, and students with accessible, responsible AI tools for teaching, learning, assessment, and discovery.
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Explore. Build. Collaborate.
Start with the demo, explore local OASIS setup, or talk with our team.
Run OASIS locally
Explore the OASIS project and plan a setup for your own teaching or research environment.
Discuss deployment
Bring your goals and questions. Let's explore what MAPLES and OASIS could look like at your institution.
Discuss Deployment
Technical setup, local workflows, and research collaboration.
Latest Updates
View all posts »Milestones, publications, and announcements from the project.
Houston Chronicle features MAPLES in coverage of UT-REAL
In the news: Julian Gill’s October 2 report features Andrew Jamieson and MAPLES in coverage of the UT-REAL initiative.
MAPLES in practice: AI‑assisted OSCE assessment
A prospective UTSW study brings multimodal AI into an objective structured clinical examination (OSCE), with targeted human review.
Three Posters and a Talk: A Successful UT REAL Health AI Symposium
Project MAPLES and the Jamieson Lab shared work on AI assessment, continuous governance, rubric design, and privacy-preserving infrastructure at the October 7–8 symposium in Austin.
Six UT Medical Schools
Collaborating across the UT System to validate AI-enabled clinical assessment.
View all participating sites →Project Aims
Better Tools. Stronger Evidence. Shared Progress.
Working across UT medical schools to develop, evaluate, and share AI-assisted assessment practices.
Share Best Practices for AI-Ready Rubrics
Share strategies and best practices for designing AI-compatible OSCE rubrics, while allowing each school to customize assessment to their own educational philosophy and clinical needs.
Scale AI Grading Infrastructure
Prepare MAPLES for governed multi-site deployment while evaluating UTSW-managed, site-specific, and UT Health Intelligence Platform (UT-HIP) infrastructure options. No cross-site production host has been selected.
Validate AI vs. Human Concordance
Conduct rigorous validation studies comparing AI grading accuracy against expert human raters across diverse patient scenarios and institutions.
Multi-Site Data Collection Planning
Define institution-specific hosting and transfer patterns, including a UT-HIP feasibility review, subject to site-specific IRB, data-use agreement, security, and hosting decisions.
Cross-Institutional Collaboration
Six UT medical schools sharing rubrics, best practices, and lessons learned — building a community of practice around AI-assisted clinical education.
Publication & Dissemination
Build on related NEJM AI and JMIR AI work, publish multi-site findings in peer-reviewed venues, and present project results at national conferences. See Research & Publications.
Program at a Glance
UTSW production and the six-school consortium.
7,000+
Encounters Graded at UTSW
6
UT Medical Schools
$300K
Grant Award
18 mo
Pilot Duration
Phase Progress
From UTSW production to governed multi-school validation.
Phase 1: UTSW Proof-of-Concept (Complete)
AI grading system developed and deployed in production at UT Southwestern, with 7,000+ encounters graded. The grant proposal estimates that the six participating medical schools collectively represent more than 3,200 medical students. A 300-encounter retrospective medRxiv preprint reported three-camera AI grading agreement of quadratic weighted kappa = 0.830 against a physician-adjudicated reference, compared with 0.732 for standard human evaluators against the same reference.
Phase 2: Site Onboarding & Approval Routing (Current)
Funded by the UT REAL Health AI Pilot Program, March 2026 ($300K, 18 months). Award setup is complete. Partner sites are moving through local IRB review, agreement routing, data preparation, and infrastructure planning at different stages. UTSW and UT-HIP technical feasibility work is also underway.
Phase 3: Initial Multi-Site Validation (Upcoming)
Begin validation with the earliest-ready, authorized site data packages while continuing site-specific technical setup and governance review.
Phase 4: Broader Validation & Dissemination
Cross-institutional validation studies, publication of multi-site results, open-source governance playbook, and framework for national adoption.
Where It All
Started
Born at the UT Southwestern Simulation Center, the MAPLES platform has graded over 7,000 clinical encounters in production. A 300-encounter retrospective multimodal preprint reported three-camera AI grading agreement of quadratic weighted kappa = 0.830 against a physician-adjudicated reference, compared with 0.732 for standard human evaluators against the same reference. Related work is published in NEJM AI and JMIR AI. Now, through the UT REAL Health AI Pilot Program, the UT-REAL project is preparing that capability for governed validation across additional UT medical schools.