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12 of 120. The UT System's health AI pilots
The UT System's health care AI laboratory funded twelve pilots for its hospitals and medical schools. Only the no-show entry at UTHealth Houston states what it already saved.
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The University of Texas System has a laboratory for health care AI, UT REAL Health AI, launched in 2025 on legislative approval and dedicated funding. It announced funding for 12 pilot projects to advance the use of AI in health care across the UT System. They were chosen from 120 systemwide submissions, and the awards total more than $3.6.
The laboratory's page carries no announcement date, so the pilots can be placed only by the day the page was read. One of its entries states a result.
What the twelve pilots put into hospitals
At UT San Antonio, a pilot deploys an AI clinical assistant called Leah across seven UT hospital sites for emergency triage and transfer decisions. The page says Leah was trained on more than 400,000 trauma cases. At UT Austin, a smartphone app listens for fetal movement to help prevent stillbirth, and the page gives its accuracy as 88% percent.
At UTMB Galveston, a pilot deploys AI referral triage. The tool reads the health record, assigns an urgency score and routes patients to a level of care. At UT Dell Medical School, a pilot on patient engagement implements six AI tools built into Epic. Among their aims are automating lab result messaging and generating patient-facing imaging summaries.
Another Dell Medical School pilot will use large language models on free-text clinical notes across more than 7 million patient records to improve suicide risk detection. At UTHealth Houston, a pilot called Priorities AI asks older adults about their surgical goals before they meet their surgeon. A wrong-tooth pilot pairs computer vision on dental images with language processing of surgical notes. A pilot led from UT Tyler uses AI to widen access to retinal screening.
Two expand platforms that already ran elsewhere. A trial prescreening platform was validated on nearly 40,000 patients at UT Southwestern. An AI grading platform for medical school simulation exams has run for more than three years and supported more than 3,500 encounters.
The one count on the page
One funded project expands AI-powered no-show reduction across UT System health institutions. The page credits it with saving more than 6,500 appointments and $900,000 in revenue at UTHealth Houston. The page cites no measurement or audit behind that figure.
The project is set to expand to UT Health San Antonio, where the page projects it will recover at least 15,000 appointments a year. The San Antonio figure is a projection and the Houston figure is a report, and the two are not the same kind of number.
What the page does not establish
Of the 12 funded projects, only the no-show entry states what it already saved. The other entries describe the work a tool does or will do, or how widely an earlier platform has run, rather than a count of what it saved.
The page does not say when the awards were made, and it does not say how any pilot will be measured or when results will be published. A reader can't tell from it which hospitals already use a given tool and which are still preparing to.
What comes next
The laboratory's 2026 symposium is set for October 7th and 8th in Austin. Its page names the laboratory's executive director, Dr. Nyma Shah of UT MD Anderson Cancer Center, and its chair, Dr. Peter McCaffrey of UTMB. The laboratory's page carries a link to register for the symposium.
Sources for this story
Claim-by-claim verification · 42 claims
UT REAL Health AI is The University of Texas Research, Engineering, and Application Laboratory for Healthcare Artificial Intelligence.
The University of Texas Research, Engineering, and Application Laboratory for Healthcare Artificial Intelligence (UT REAL Health AI)
The University of Texas System launched UT REAL Health AI in 2025 as a systemwide initiative after it received legislative approval and dedicated funding.
In 2025, the University of Texas System received legislative approval and dedicated funding to launch UT REAL Health AI, a systemwide initiative to responsibly accelerate artificial intelligence in health.
Dr. Nyma Shah of UT MD Anderson Cancer Center is the executive director of UT REAL Health AI.
Executive Director: Dr. Nyma Shah, UT MD Anderson Cancer Center
Dr. Peter McCaffrey of UTMB chairs UT REAL Health AI.
Chair: Dr. Peter McCaffrey, UT Medical Branch (UTMB)
Zain Kazmi of the UT System Office of Health Affairs is the executive sponsor of UT REAL Health AI.
Executive Sponsor: Zain Kazmi, UT System Office of Health Affairs
UT REAL Health AI announced funding for 12 pilot projects to advance the use of AI in health care across the UT System. The release page carries no date.
today announced funding for 12 pilot projects designed to advance the use of AI in healthcare across the UT System
The 12 pilot awards were chosen through a two-phase review of 120 systemwide submissions.
Selected through a rigorous two-phase review of 120 systemwide submissions
The pilot awards total more than $3.6 million across eleven UT institutions.
Competitive awards totaling more than $3.6 million support AI innovation in emergency care, patient safety, clinical efficiency, and medical education across eleven UT institutions.
Three of the 12 projects, described as moonshots, each received up to $500,000.
Three moonshot projects each received up to $500,000 to pursue transformative clinical AI.
Bedside Emergency AI for Care Orchestration is a funded pilot at UT San Antonio led by Dr. Amina Qutub.
Bedside Emergency AI for Care Orchestration (UT San Antonio, PI: Dr. Amina Qutub
The UT San Antonio pilot puts an AI clinical assistant called Leah across seven UT hospital sites for emergency triage and transfer decisions. The page says Leah was trained on more than 400,000 trauma cases.
deploys "Leah," an AI clinical assistant trained on more than 400,000 trauma cases, across seven UT hospital sites to optimize emergency triage and transfer decisions
A smartphone tool that uses AI to detect fetal movements to help prevent stillbirth is a funded pilot at UT Austin led by Dr. Kenneth Moise.
Smartphone Detection of Fetal Movements Using Artificial Intelligence: A Novel Approach to Prevent Stillbirth (UT Austin, PI: Dr. Kenneth Moise
A UT Southwestern pilot led by Dr. Dylan Owens works on automating clinical registry abstraction with AI.
Reimagining Clinical Registry Abstraction with Uncertainty-Aware, Self-Improving AI (UT Southwestern, PI: Dr. Dylan Owens
At UTMB Galveston, Dr. Salim Hayek leads a funded pilot that deploys AI referral triage to reduce low-value care and improve patient access.
At UTMB Galveston, Dr. Salim Hayek is deploying AI Referral Triage to Reduce Low-Value Care and Improve Patient Access
At UTHealth Houston, Dr. Aanand Naik leads a funded pilot that scales Priorities AI toward Medicare age-friendly health care designation.
At UTHealth Houston, Dr. Aanand Naik is scaling Priorities AI Scaling and Implementation to Achieve Medicare Age-Friendly Healthcare Designation
At UT Dell Medical School, Dr. Salim Saiyed leads a funded pilot on an AI tool for patient engagement and clinical efficiency.
At UT Dell Medical School, Dr. Salim Saiyed is leading AI Tool for Patient Engagement and Clinical Efficiency Enhancement
A funded pilot to expand AI-powered no-show reduction across UT System health institutions is led by Dr. Xiaoqian Jian, as the page spells it.
Expanding AI-Powered No-Show Reduction Across UT System Health Institutions, led by Dr. Xiaoqian Jian
A pilot led by Dr. Ming Huang at UT Dell Medical School will use large language models on free-text clinical notes across more than 7 million patient records. It aims to improve suicide risk detection.
led by Dr. Ming Huang at UT Dell Medical School will use large language models to analyze free-text clinical notes across more than 7 million patient records. The system will identify suicidal ideation and life stressors, including unemployment and financial strain, to improve suicide risk detection
An AI early warning system against wrong-tooth extraction in oral and maxillofacial surgery is a funded pilot led by Dr. Sayali Tungare at UTHealth Houston.
Early Warning System to Predict Wrong-Tooth Extraction (WTE) in Oral & Maxillofacial Surgery (Predict-WTE) led by Dr. Sayali Tungare at UTHealth Houston
One funded pilot expands an AI clinical trial prescreening platform first validated on nearly 40,000 patients at UT Southwestern.
expands a clinical trial prescreening platform previously validated on nearly 40,000 patients at UT Southwestern
One funded pilot expands an AI grading platform for medical school simulation exams. The platform has been in continuous use for more than three years and has supported more than 3,500 encounters.
expands an AI grading platform that has been in continuous operational use for more than three years and has supported more than 3,500 simulation encounters
A pilot led by Dr. Andrea Cooley at UT Tyler uses AI to expand access to retinal screening.
led by Dr. Andrea Cooley at UT Tyler, uses artificial intelligence to expand access to retinal screening and early disease detecting at scale
The UT REAL Health AI page says the no-show reduction project has already saved more than 6,500 appointments and $900,000 in revenue at UTHealth Houston. The page cites no measurement or audit behind the figure.
that has already saved more than 6,500 appointments and $900,000 in revenue at UTHealth Houston
The no-show project is set to expand to UT Health San Antonio, where the page projects it will recover 15,000 to 20,000 appointments a year.
The project will now expand to UT Health San Antonio, where it is projected to recover 15,000–20,000 appointments annually.
The 2026 UT REAL Health AI Symposium is set for October 7th and 8th in Austin.
2026 UT REAL Health AI Symposium - October 7-8, Austin, TX
ERCOT told its board that it has paused energizing new large load data centers and crypto mining facilities until its verification and audit and its community impact review are complete.
ERCOT has paused the energization of new Large Load data centers and crypto mining facilities until the completion of the verification and audit and community impact review.
ERCOT listed among its actions a pause on approvals to energize data centers and crypto mining facilities of 75 MW or more.
Paused approvals to energize data centers or virtual currency mining facilities (crypto facilities) that are 75 MW or greater.
ERCOT said the pause covers large load data centers and crypto facilities that are not yet connected to its system.
Energization pause impacts Large Load data centers and crypto facilities not yet connected to the ERCOT system.
ERCOT said it will file its Batch Zero eligibility verification report with the Public Utility Commission of Texas by December 10th, 2026.
ERCOT will file the Batch Zero Eligibility Verification Report with the Commission by December 10, 2026.
ERCOT said it will file a community impact review report with the Public Utility Commission of Texas by December 10th, 2026.
ERCOT will file the Community Impact Review Report with the Commission by December 10, 2026.
The Batch Zero update went to the ERCOT Board of Directors meeting of September 14th to 15th, 2026.
Board of Directors Meeting September 14-15, 2026
A release on NextEra's newsroom dated September 30th, 2026 says the U.S. Department of Commerce and the Republic of Korea selected Related Companies and NextEra Energy Resources, with Lewis Energy Group, to develop Project Star in Encinal.
The U.S. Department of Commerce and the Republic of Korea selected Related Companies and NextEra Energy Resources, in partnership with Lewis Energy Group, to develop Project Star
The companies say Project Star will build 6.47 GW of natural gas generation to serve an adjacent 5 GW campus and send excess power to the grid.
Project Star will create 6.47 GW of natural gas generation to directly support an adjacent 5 GW digital infrastructure campus and deliver excess power generation back to the grid.
The release says Related Digital is developing the privately funded 5 GW data center campus on an adjacent site.
At full scale, Project Star will support a privately funded 5 GW data center campus that Related Digital is developing on an adjacent site.
The release says initial generation could come online as early as 2029 and remains subject to required permitting and approvals.
initial generating resources anticipated to come online as early as 2029, subject to required permitting and approvals
The UTMB referral triage tool reads health record documentation, assigns an urgency score and routes each patient to a level of care.
The tool analyzes EHR documentation, assigns urgency scores, and routes patients to the appropriate level of care.
The UTHealth Houston Priorities AI pilot is a conversational agent that asks older adults about their surgical goals before they meet their surgeon.
a conversational agent that identifies older adults' surgical outcome goals before meeting with their surgeon
The Dell Medical School pilot implements six AI tools built into Epic. Among their aims are automating lab result messaging and generating patient-facing imaging summaries.
which implements six Epic-integrated AI tools to automate lab result messaging, generate patient-facing imaging summaries
The wrong-tooth pilot pairs computer vision on dental images with language processing of surgical notes.
will combine computer vision analysis of dental imaging with natural language processing of surgical documentation to help prevent wrongful tooth extraction
The stillbirth pilot is a smartphone app that listens for fetal movements, with an accuracy the page gives as 88%.
is a smartphone app that uses AI-powered acoustic analysis to detect fetal movements with 88% accuracy
ERCOT said it acted on the Governor's August 3rd, 2026 directive on connecting data centers and crypto mining loads.
ERCOT took immediate actions to address Governor Abbott's August 3, 2026 directive related to the interconnection of data centers and crypto mining loads.
The UT REAL Health AI home page links to registration for the 2026 symposium.
Visit Symposium Site | Register Now | Submit an Abstract