JMIR Publications

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 improve access to personalised risk communication. Visualisation 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 non-expert operators.

Objective:

This study aimed to describe an iterative user-centred design process enabling non-expert use of AI-assisted carotid ultrasound for visualising subclinical atherosclerosis in primary care cardiovascular disease prevention.

Methods:

A user-centred, iterative study was conducted within the Västerbotten Intervention Programme in northern Sweden. Fifteen nurse assistants from four primary care centres representing urban, rural, and remote rural settings tested an AI-assisted carotid ultrasound prototype with real-time guidance and artery segmentation in their routine clinical work environment. Data were collected through structured system-interaction observations and semi-structured interviews. Interview data were analysed using thematic analysis, while structured observation data were used to identify usability and workflow issues that informed iterative refinements.

Results:

The thematic analysis of interview data identified four categories 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-centred study identified key practical, organisational, and educational conditions for integrating AI-assisted carotid ultrasound into routine preventive care by non-expert staff in primary care. Successful implementation depends 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.