AI-Assisted Carotid Ultrasound for Nonexpert Use in Primary Care Cardiovascular Prevention: User-Centered Iterative Development Study.
8 affiliations
- 1Department of Nursing, Umeå University, Umea, Västerbotten, Sweden.
- 2Department of Diagnostics and Intervention, Umeå University, Umea, Västerbotten, Sweden.
- 3Västerbotten County, Umea, Västerbotten, Sweden.
- 4Department of Public Health and Clinical Medicine, Umeå University, Umea, Västerbotten, Sweden.
- 5Department of Epidemiology and Global Health, Umeå University, Umea, Västerbotten, Sweden.
- 6Umeå University, Umea, Västerbotten, Sweden.
- 7Department of Public health and Clinical Medicine, Umeå University, Umea, Västerbotten, Sweden.
- 8Department of Health and Medical Care, Region Norrbotten, Luleå, Norrbotten, Sweden.
Abstract
Background
Carotid ultrasound is traditionally confined to specialist settings because examination quality and diagnostic reliability are highly dependent on the operator's technical skill and experience. Recent advances in AI-assisted ultrasound may enable task shifting to primary care staff and support the integration of personalized visual risk communication into routine preventive care. Visualization of subclinical atherosclerosis has been shown to strengthen cardiovascular risk communication and support preventive engagement. However, limited evidence exists on how such technology can be practically integrated into routine primary care workflows and what conditions are required to support use by nonexpert operators.Objective
This study aimed to describe an iterative user-centered design process enabling nonexpert use of AI-assisted carotid ultrasound for visualizing subclinical atherosclerosis in primary care cardiovascular disease prevention.Methods
We conducted a user-centered, iterative study within the Västerbotten Intervention Programme in northern Sweden. Fourteen nurse assistants from 4 primary care centers representing urban, rural, and remote rural settings tested an AI-assisted carotid ultrasound prototype with real-time guidance and artery segmentation in their primary care work environment, while 15 nurse assistants participated in semistructured interviews. Structured system-interaction observations were conducted during prototype testing. Interview data were analyzed using inductive thematic analysis informed by Braun and Clarke's approach, while data from structured system-interaction observations were analyzed pragmatically to identify usability and workflow issues relevant to the iterative development process.Results
The thematic analysis of interview data identified 4 themes describing nurse assistants' experiences and conditions for implementation: technology that fits the everyday primary care work; system guidance as a prerequisite for effective use; practical, peer-supported learning for confidence in clinical use; and confidence in the patient encounter. Observations identified recurring usability challenges related to ergonomics, probe handling, interface navigation, image acquisition, and time pressure. These findings led to iterative refinements, including redesigned interface navigation, simplified data entry, added audio feedback, enhanced probe support, and revised training materials.Conclusions
This user-centered study identified key practical, organizational, and educational conditions for integrating AI-assisted carotid ultrasound into routine preventive care by nonexpert staff in primary care. The findings suggest that successful implementation will depend not only on technical capability but also on ergonomic and workflow fit, intuitive system guidance, peer-supported learning, and confidence in patient communication. These findings provide a foundation for future validation and implementation studies.
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BibTeX
@article{sjostrom2026aiassisted,
title = {{AI-Assisted Carotid Ultrasound for Nonexpert Use in Primary Care Cardiovascular Prevention: User-Centered Iterative Development Study.}},
author = {Anna Sjöström and Christer Grönlund and Karolina Jonzén and Patrik Wennberg and Åsa Hörnsten and Thorbjörn Lundberg and Anni-Maria Pulkki-Brännström and Luisa Ebeling and Albin Dahlin Almevall},
journal = {Journal of medical Internet research},
year = {2026},
month = oct,
doi = {10.2196/105250},
url = {https://europepmc.org/article/MED/42849032},
note = {CC BY},
}
RIS
TY - JOUR
TI - AI-Assisted Carotid Ultrasound for Nonexpert Use in Primary Care Cardiovascular Prevention: User-Centered Iterative Development Study.
AU - Anna Sjöström
AU - Christer Grönlund
AU - Karolina Jonzén
AU - Patrik Wennberg
AU - Åsa Hörnsten
AU - Thorbjörn Lundberg
AU - Anni-Maria Pulkki-Brännström
AU - Luisa Ebeling
AU - Albin Dahlin Almevall
PY - 2026
DA - 2026/10/08
JO - Journal of medical Internet research
DO - 10.2196/105250
UR - https://europepmc.org/article/MED/42849032
AB - Background Carotid ultrasound is traditionally confined to specialist settings because examination quality and diagnostic reliability are highly dependent on the operator's technical skill and experience. Recent advances in AI-assisted ultrasound may enable task shifting to primary care staff and support the integration of personalized visual risk communication into routine preventive care. Visualization of subclinical atherosclerosis has been shown to strengthen cardiovascular risk communication and support preventive engagement. However, limited evidence exists on how such technology can be practically integrated into routine primary care workflows and what conditions are required to support use by nonexpert operators.Objective This study aimed to describe an iterative user-centered design process enabling nonexpert use of AI-assisted carotid ultrasound for visualizing subclinical atherosclerosis in primary care cardiovascular disease prevention.Methods We conducted a user-centered, iterative study within the Västerbotten Intervention Programme in northern Sweden. Fourteen nurse assistants from 4 primary care centers representing urban, rural, and remote rural settings tested an AI-assisted carotid ultrasound prototype with real-time guidance and artery segmentation in their primary care work environment, while 15 nurse assistants participated in semistructured interviews. Structured system-interaction observations were conducted during prototype testing. Interview data were analyzed using inductive thematic analysis informed by Braun and Clarke's approach, while data from structured system-interaction observations were analyzed pragmatically to identify usability and workflow issues relevant to the iterative development process.Results The thematic analysis of interview data identified 4 themes describing nurse assistants' experiences and conditions for implementation: technology that fits the everyday primary care work; system guidance as a prerequisite for effective use; practical, peer-supported learning for confidence in clinical use; and confidence in the patient encounter. Observations identified recurring usability challenges related to ergonomics, probe handling, interface navigation, image acquisition, and time pressure. These findings led to iterative refinements, including redesigned interface navigation, simplified data entry, added audio feedback, enhanced probe support, and revised training materials.Conclusions This user-centered study identified key practical, organizational, and educational conditions for integrating AI-assisted carotid ultrasound into routine preventive care by nonexpert staff in primary care. The findings suggest that successful implementation will depend not only on technical capability but also on ergonomic and workflow fit, intuitive system guidance, peer-supported learning, and confidence in patient communication. These findings provide a foundation for future validation and implementation studies.
SN - 1439-4456
SN - 1438-8871
ER -