Privacy-preserving training could expand medical AI for children while keeping data local

by Laura Castañón, Harvard Medical School

edited by Swati Mestri, reviewed by Robert Egan

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In the field of radiology, AI models have the potential to improve imaging analysis, speed up diagnoses and catch errors that might otherwise be missed. But the vast majority of these models are trained on adult data and don't translate well to infants and children, keeping these advances out of reach of neonatal and pediatric care.

"Because it's so much harder to get data for children, this transformative technology is disproportionately being developed to help adults," said Vishwanatha Rao, a member of Pranav Rajpurkar's biomedical informatics lab at Harvard Medical School. "Children are being left behind."

A large part of the reason for the gap is that medical records on newborns and children are subject to strict privacy regulations and tend to remain within the institution where the child is a patient, leaving data less accessible for training robust AI models. According to Rao and colleagues, children represent less than 1% of public medical imaging datasets.

Rao led and is co-first author of a new study, published in Nature Biomedical Engineering, showing that models trained on adult radiographs can be adapted for infants using data from multiple hospitals without sensitive information ever needing to leave each institution. The model travels, but the data stays in-house.

The team believes its proof-of-principle approach could be extended to adapt other adult-trained AI models for pediatric use, addressing critical gaps in the deployment of pediatric medical AI.

"Our goal is to figure out how to make medical AI models work for everyone, including vulnerable populations where data can't easily be shared," said senior author Rajpurkar, associate professor of biomedical informatics in the Blavatnik Institute at HMS. "Now we've come up with a method that would allow every institution to keep its data within its walls."

Training sequentially

The researchers started with a model that had been trained on adult data to assess whether breathing tubes have been placed correctly. Breathing tubes, or endotracheal tubes, are crucial for treating respiratory issues, and there is little margin for error, especially in newborns.

Studies have shown that AI models can quickly flag potential issues and shorten the time it takes for radiologists to review and correct tube placement. Multiple AI tools have already received FDA clearance for this task in adults, but none of them are cleared for infants.

Rajpurkar and colleagues collected 2,965 chest X-rays from 30 neonatal intensive care units across 20 countries and territories. To simulate a scenario in which the data never leaves the hospital, the researchers trained the model sequentially on each institution's data.

Despite differences in image quality and characteristics, the new model outperformed adult models trained on pediatric data from a single hospital at 29 of 30 sites, even when tested on that hospital's own images, which the new model had never seen.

The fully trained model was able to identify the location of the tip of the endotracheal tube—used to gauge whether the tube is too shallow or too deep—with an average error of 0.67 cm. Existing models adapted to children with data from a single institution averaged errors of 1.29 cm, and adult models were off by an average of 4.30 cm.

"We tested the model on a hospital it had never seen before, and it did better than a model trained on scans from that specific hospital—that's a pretty powerful result," said Rao, who is an M.D.-Ph.D. student at the University of Pennsylvania. "And compared to the original adult model, it's leaps and bounds better."

Medical AI for all

Only 17% of FDA-authorized AI medical devices are labeled for use in children. While this particular model was trained on endotracheal tube placement, the approach could be generalized to adapt other adult models for pediatric use, the researchers said.

"This is a general framework that shows a cheap, privacy-preserving approach to take advantage of existing adult information and get it to work on children," Rao said. "It could usher in a whole new generation of AI models that work for kids, adapted through this broad methodology."

The method isn't limited to pediatrics, either. The same framework could potentially be applied to any patient population that is underrepresented in AI training data.

"A lot of what research is meant to accomplish is showing that something is possible," Rajpurkar said. "We've shown there's a system by which you can tune these models without sensitive data leaving a hospital."

Publication details

Vishwanatha M. Rao et al., Repurposing adult AI radiograph models for neonatal care through continual learning: an international multicentre study, Nature Biomedical Engineering (2026). DOI: 10.1038/s41551-026-01812-6. www.nature.com/articles/s41551-026-01812-6

Journal information: Nature Biomedical Engineering

Key medical concepts

Chest Radiography

Clinical categories

PediatricsDiagnostic radiologyChildren's health Provided by Harvard Medical School Who's behind this story?

Swati Mestri

Swati Mestri holds a bachelor's degree in Electronics Engineering and has worked as a content editor since 2019. She has experience editing research documents across technology, health care, and materials science, and has a particular interest in technology and space. Full profile →

Robert Egan

Bachelor's in mathematical biology, Master's in creative writing. Well-traveled with unique perspectives on science and language. Full profile →

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