Funded by UT REAL Health AI · University of Texas System
The MAPLES AI grading platform is being prepared for multi-site validation across UT System medical schools, with engagement through the UT Health Intelligence Platform underway and funding from the UT-REAL Health AI initiative.
Partnering across the UT System to validate AI-enabled clinical assessment.
Milestones, publications, and announcements from the project.
Watch Minhan Park and Licheng Yi demonstrate Wayfinder: create a structured rubric from a short description, enhance it with accept/reject suggestions, transform it for a new scenario, and continue in chat.
On Healthcare AI Pioneers, Thomas Dalton and Andrew Jamieson discuss UT Southwestern’s AI-enabled OSCE assessment work: rubrics, multimodal evidence, staged rollout, human review, and precision education.
Watch Ameer Hamza Shakur walk through the MAPLES workflow: upload a rubric, group OSCE encounters, run AI-assisted grading, review cited evidence, and export validated results.
Collaborating across the UT System to validate AI-enabled clinical assessment.
View all participating sites →Our Mission
Our mission is to give medical educators better tools so they can give students better feedback — faster, more consistent, and at a scale that wasn't possible before.
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.
Prepare MAPLES for governed multi-site deployment across partner sites, with the UT Health Intelligence Platform (UT-HIP) as the preferred infrastructure path under active planning.
Conduct rigorous validation studies comparing AI grading accuracy against expert human raters across diverse patient scenarios and institutions.
Evaluate UT-HIP as the preferred hosting path for a governed multi-institutional OSCE dataset, pending site-specific IRB, DUA, and hosting decisions.
Six UT medical schools sharing rubrics, best practices, and lessons learned — building a community of practice around AI-assisted clinical education.
Publish findings in high-impact venues (NEJM AI, JMIR AI) and present at national conferences to advance the field of AI in medical education.
Production metrics from UTSW — the foundation we're scaling across the UT System.
From single-site proof-of-concept to UT System-wide deployment.
Phase 1: UTSW Proof-of-Concept (Complete)
AI grading system developed and deployed in production at UT Southwestern. 7,000+ encounters graded, 3,200+ students assessed. Published in NEJM AI and JMIR AI. AI agreement (kappa = 0.830) exceeds human inter-rater reliability (kappa = 0.732).
Phase 2: Award & Planning (Current)
Funded by the UT REAL Health AI Pilot Program, March 2026 ($300K, 18 months). Award setup complete. Infrastructure engagement with UT-HIP and UTSW Enterprise Data Services underway. IRB template protocol in review. Site inventory and governance planning in progress.
Phase 3: Multi-Site Deployment
Phased onboarding of partner sites beginning with Wave 1 institutions. Site-specific technical audits, IRB/DUA routing, and data ingestion pipeline setup.
Phase 4: Validation & Dissemination
Cross-institutional validation studies, publication of multi-site results, open-source governance playbook, and framework for national adoption.
Born at the UT Southwestern Simulation Center, the MAPLES platform has graded over 7,000 clinical encounters in production — proving that AI can match and exceed human inter-rater reliability. Published in NEJM AI and JMIR AI, with multimodal assessment research on preprint. Now, through the UT-REAL initiative and the UT Health Intelligence Platform, we're scaling that capability across the UT System.
