Texas Children's and UT Southwestern radiologists publish that imaging AI has largely skipped children
An expert panel review in the American Journal of Roentgenology states that pediatric medical imaging remains substantially underrepresented across artificial intelligence in radiology. The panel means the development of models and their validation and their regulation and their use in the clinic. Two of its authors are Texas radiologists. One works in the radiology department shared by Baylor College of Medicine and Texas Children's Hospital in Houston. The other is at UT Southwestern Medical Center in Dallas. The panel gives its reason plainly. Children go through continuous physiologic and anatomic changes that adults do not, so a model trained on adults is not the model a child needs. It names what holds pediatric work back. Public datasets are scarce and institutional data is fragmented and external validation is insufficient. It also names off-label use of adult-trained AI models among the ethical and regulatory concerns in children. The review is a recommendation document rather than a hospital policy. No Texas institution has published a purchasing change that follows from it.
How to take part
The review is published in the American Journal of Roentgenology. The abstract and the author affiliations are readable without a subscription through the National Library of Medicine.
Where
2 counties. In Dallas-Fort Worth-Arlington, Houston-Pasadena-The Woodlands.
Timeline
- filed
Article date recorded by the National Library of Medicine for the online publication
- Today
How this decision moved
One dated line per check, oldest first. A line that says nothing changed means somebody looked and it had not.
- 2026-09-13
Admitted to the record. Texas radiologists at two of the state's largest institutions have put in print that imaging AI has largely skipped children. The same review names adult trained models being used on children off label. Nothing follows from the review on its own and no Texas hospital has published a change beside it.
- 2026-09-16
The finding that imaging artificial intelligence has largely skipped children is still published as written. No correction has appeared against it.
- 2026-09-19
The finding stands as published, and nothing has been announced to close the gap it names in imaging for children.
- 2026-09-23
The finding that imaging tools have largely skipped children still stands as published.
- 2026-09-26
The published finding that imaging AI has largely skipped children still stands.
The evidence
Every fact above rests on one of these. The words are the source's own.
Artificial intelligence (AI) applications have transformed radiology, yet pediatric medical imaging remains substantially underrepresented in AI development, validation, regulation, and implementation.Pediatric Artificial Intelligence in Radiology: Unmet Needs and Next Steps-AJR Expert Panel Review, National Library of Medicine Primary source, official · eutils.ncbi.nlm.nih.gov
Unlike adults, children go through continuous physiologic and anatomic changes that require age-specific models trained on representative developmental data.Pediatric Artificial Intelligence in Radiology: Unmet Needs and Next Steps-AJR Expert Panel Review, National Library of Medicine Primary source, official · eutils.ncbi.nlm.nih.gov
However, pediatric AI is limited by scarce publicly available datasets, fragmented institutional data, rare diseases, heterogeneous reporting practices, and insufficient external validation.Pediatric Artificial Intelligence in Radiology: Unmet Needs and Next Steps-AJR Expert Panel Review, National Library of Medicine Primary source, official · eutils.ncbi.nlm.nih.gov
Ethical and regulatory challenges are also a concern in children, including consent for secondary data use, off-label use of adult-trained AI models, and the need for postdeployment surveillance.Pediatric Artificial Intelligence in Radiology: Unmet Needs and Next Steps-AJR Expert Panel Review, National Library of Medicine Primary source, official · eutils.ncbi.nlm.nih.gov
The panel gives key recommendations, emphasizing the importance of an implementation roadmap to establish a dedicated pediatric AI infrastructure and standards that are essential to ensure diagnostic accuracy, workflow efficiency, and optimal clinical outcomes for children while minimizing bias and protecting patient safety.Pediatric Artificial Intelligence in Radiology: Unmet Needs and Next Steps-AJR Expert Panel Review, National Library of Medicine Primary source, official · eutils.ncbi.nlm.nih.gov
Department of Radiology, Baylor College of Medicine and Texas Children's Hospital, Houston, Texas, USA.Pediatric Artificial Intelligence in Radiology: Unmet Needs and Next Steps-AJR Expert Panel Review, National Library of Medicine Primary source, official · eutils.ncbi.nlm.nih.gov
Department of Radiology, UT Southwestern Medical Center, Dallas, Texas, USA.Pediatric Artificial Intelligence in Radiology: Unmet Needs and Next Steps-AJR Expert Panel Review, National Library of Medicine Primary source, official · eutils.ncbi.nlm.nih.gov
Additionally, reimbursement is misaligned and must be optimized to allow innovation.Pediatric Artificial Intelligence in Radiology: Unmet Needs and Next Steps-AJR Expert Panel Review, National Library of Medicine Primary source, official · eutils.ncbi.nlm.nih.gov
Questions about this decision
Answered from the record itself. Every answer is assembled from stored fields, so an answer the record has no basis for is left out rather than guessed.
What is this decision?
An expert panel review in the American Journal of Roentgenology states that pediatric medical imaging remains substantially underrepresented across artificial intelligence in radiology. The panel means the development of models and their validation and their regulation and their use in the clinic. Two of its authors are Texas radiologists. One works in the radiology department shared by Baylor College of Medicine and Texas Children's Hospital in Houston. The other is at UT Southwestern Medical Center in Dallas. The panel gives its reason plainly. Children go through continuous physiologic and anatomic changes that adults do not, so a model trained on adults is not the model a child needs. It names what holds pediatric work back. Public datasets are scarce and institutional data is fragmented and external validation is insufficient. It also names off-label use of adult-trained AI models among the ethical and regulatory concerns in children. The review is a recommendation document rather than a hospital policy. No Texas institution has published a purchasing change that follows from it.
Who decides it?
American Journal of Roentgenology Expert Panel, with radiologists at Baylor College of Medicine and Texas Children's Hospital and at UT Southwestern Medical Center decides. The record names the deciding body for every entry it carries.
Can the public take part?
The review is published in the American Journal of Roentgenology. The abstract and the author affiliations are readable without a subscription through the National Library of Medicine. No dated public window is on the record. The deciding body is named and reachable.
Where in Texas does it apply?
It covers Dallas and Harris Counties.
Has it been decided?
It has been decided. The dates on the item page carry when.
What happens next?
No future date is on the record. The last dated step on it was filed on September 9th.
When did it start?
The earliest date on its record is September 9th, 2026.
What kind of decision is it?
It is filed under health and education.
What sources back it?
One source backs it. It is primary.
Is it on the ERCOT grid?
Yes. It sits inside the ERCOT interconnection.
When was it last checked?
Every fact on it was last verified against its source on September 26th, 2026.
Cite this
Texas AI Docket, Texas Children's and UT Southwestern radiologists publish that imaging AI has largely skipped children. Tracked since September 9th, 2026. Last verified September 26th, 2026. https://texasaidocket.com/item/tx-2026-0150/. Reuse permitted under CC BY 4.0 with attribution. The same entry is in the docket JSON as item tx-2026-0150.
Beat
Filed under Health and education, with every other decision on that beat.