Non-experts with AI guidance and off-site review perform quality mobile rural heart scans
· Medical Xpressedited by Swati Mestri, reviewed by Andrew Zinin
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Artificial intelligence (AI) helped health professionals with no ultrasound experience capture quality heart scans in rural communities for review by off-site experts, according to new, independent preliminary research to be presented at the American Heart Association's Scientific Sessions 2026. The meeting takes place Nov. 6–9 in Chicago.
Echocardiography, or ultrasound imaging of the heart, performed with AI assistance and reviewed by experts in other locations may be one way to bring heart imaging to people living in rural communities. Echocardiography uses ultrasound to provide live, moving images of the heart's structure and function.
According to the American Heart Association, your health care professional may order this test if you have a heart murmur, damaged heart valves, unexplained chest pain, a congenital heart defect, a history of heart attack or stroke, or rheumatic fever.
"Echocardiography is one of the most useful tools we have in cardiovascular research; however, it has historically been very hard to deliver in rural communities," said study author Dr. Sanjana Ravi, an internal medicine and pediatrics resident at the Duke University School of Medicine in Durham, North Carolina.
"The conventional model depends on an expert sonographer working in a hospital setting or imaging center, which can be limited in rural areas. There is also a shortage of cardiovascular imaging specialists in these regions to review and evaluate images, as well as clinicians to share results with patients."
The current study described how a quality assurance program, including expert review at the Duke Clinical Research Institute Imaging Core Laboratory, supported nonsonographer health care staff in performing AI-guided echocardiography in a mobile examination unit in rural areas. The research is part of the large RURAL (Risk Underlying Rural Areas Longitudinal) study.
What are the study's main findings?
- In heart scans for 4,597 volunteer participants in the RURAL study, 98% of the scans included all six standard echocardiography views required by the study protocol, the current standard practice. Studies were rated adequate (ability to perform measurements on the image) by the core laboratory experts more than 99% of the time. Rates of diagnostic-quality images for individual views ranged from 77% to 94%.
- Scans were performed within a 15-minute target window 81% of the time, with a median time of 9.5 minutes.
- Critical results were identified in 133 scans among study volunteers, who were scanned as part of the research study rather than for a medical reason. The most common finding was an ejection fraction of less than 50%, indicating that the heart's main pumping chamber pushes out a lower-than-normal percentage of its blood with each heartbeat. Of these critical results, 99% were returned to participants within three days.
"We were surprised at how quickly and well the nurses and CT technologists who had never done a heart ultrasound were able to adapt to this technology and produce quality images for review by experts," Ravi said. "With real-time AI guidance and a rigorous quality-control system behind it, a nurse or CT technologist in a mobile unit can produce heart images good enough for research and good enough to flag serious heart problems that need follow-up, bringing the imaging to the community rather than asking the community to travel to it."
Dr. Lee H. Schwamm, FAHA, a member of the American Heart Association's volunteer committee on AI and Technology Innovation, said, "One in five Americans lives in a rural area, which limits their access to critical diagnostic testing and specialty care. In this hybrid model, community-based nonexperts were able to create a human-AI partnership with the acquisition software to enable the capture of echocardiogram images that could be read by off-site experts.
"This novel approach brings heart imaging to local communities and truly meets people where they are. If confirmed in further studies, this approach could serve as a model for other specialties and diagnostic modalities."
Schwamm, who was not involved in this study, is also senior vice president and chief digital health officer of the Yale New Haven Health System and associate dean of digital strategy and transformation at Yale School of Medicine.
Study details, background, design and limitations:
- Volunteer participants in the RURAL study included 4,597 adults ages 25–65; the average age was 48, and 67% were women. At enrollment, 63% of participants self-identified as white adults, 35% as Black adults and 2% as Hispanic adults.
- The larger cohort study included physical exams and tests related to heart, lung and blood vessel health. The 14 nonsonographers (nurses, nursing assistants and CT technicians) completed the two-hour training in AI-guided ultrasound; three needed retraining after review of their first scans.
- Each participant's exams and testing were conducted in one 3.5-hour appointment at a mobile health unit that visited 10 rural counties in Alabama, Kentucky, Louisiana and Mississippi between November 2021 and June 2026.
- All participants received echocardiograms performed by health professionals who were not sonographers. Professionals were trained in a two-hour, four-part curriculum that included both virtual and hands-on components in the mobile health unit on how to capture quality heart ultrasound images with real-time guidance from AI-driven software.
- Images were reviewed by experienced sonographers and physicians with echocardiography certification for completeness and quality, and to measure heart structures. The experts were part of the Duke Clinical Research Institute (DCRI) Imaging Core Laboratory, a specialized team that processes and manages cardiovascular imaging for research studies.
- The study assessed whether research-quality images could be obtained in a timely manner (with a target time of under 15 minutes).
- The study has several limitations. There was no direct comparison between sonographer-acquired images and those from AI-guided nonsonographers. The reproducibility of image measurements was also assessed internally and was not validated against other core laboratories. Variability between different operators scanning the same participant was not systematically assessed. Only one brand of software was used, and only one core laboratory was involved, so adapting the strategy to other platforms would require individual testing and validation. The results may not be generalizable to nonrural populations and to AI-guided echocardiograms obtained outside of a core lab–supported research setting. The study also did not evaluate whether returning participants' results influenced their care or health.
Dr. Gerald S. Bloomfield, M.P.H., is the study's principal investigator and is an associate professor of medicine at the Duke University School of Medicine.
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CardiologyCommon illnesses & Prevention Provided by American Heart Association 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 →
Andrew Zinin
Master's in physics with research experience. Long-time science news enthusiast. Plays key role in Science X's editorial success. Full profile →
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