Health professionals with no prior ultrasound training successfully captured quality heart scans in rural communities using artificial intelligence (AI) guidance, according to preliminary research presented at the American Heart Association's Scientific Sessions 2026. The findings, part of the RURAL (Risk Underlying Rural Areas Longitudinal) study, suggest a potential solution to the persistent challenge of delivering specialized cardiac imaging to underserved areas.
The study involved 14 non-sonographers—nurses, nursing assistants, and CT technologists—who completed a two-hour training program on AI-guided ultrasound. They performed Echocardiography scans on 4,597 volunteer participants in a mobile health unit that traveled to 10 rural counties across Alabama, Kentucky, Louisiana, and Mississippi. The scans were reviewed remotely by experts at the Duke Clinical Research Institute Imaging Core Laboratory. According to the American Heart Association, echocardiography uses ultrasound to provide live, moving images of the heart's structure and function.
Results showed that 98% of the scans included all six standard echocardiography views required by the study protocol, and more than 99% were rated adequate for measurement by core laboratory experts. Diagnostic-quality images for individual views ranged from 77% to 94%. Scans were completed within a 15-minute target window 81% of the time, with a median time of 9.5 minutes. Critical findings were identified in 133 scans, most commonly an ejection fraction below 50%, indicating reduced heart pumping function. 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,” said study author Sanjana Ravi, M.D., an internal medicine and pediatrics resident at Duke University School of Medicine. “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.”
The implications for rural health care are significant. Lee H. Schwamm, M.D., FAHA, a member of the American Heart Association's volunteer committee on AI and Technology Innovation, noted that “1 in 5 Americans lives in a rural area, which limits their access to critical diagnostic testing and specialty care.” He added that this hybrid model “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 was not involved in the study.
The research is considered preliminary because abstracts presented at the Association's scientific meetings are not peer-reviewed. The study also has limitations, including no direct comparison between sonographer-acquired and AI-guided images, use of a single software brand, and a lack of assessment on whether returning results influenced care. The full abstract is available via abstract poster MDP2290. Additional multimedia, including video interview clips with Schwamm, can be found on the right column of the release link. The Association's financial information is available here.
