What Should the Ultrasound Workstation of the Future Look Like? Reflections from a Practicing Ultrasound Physician
This perspective article, written by a practicing clinical ultrasound physician, asks a plain but fundamental question: if we were to redesign an ultrasound workstation from scratch, what should it look like? The article argues that the future of ultrasound AI should go beyond "recognizing images more accurately" toward a navigation-like assistant: one that knows in real time where the probe is, what has been examined, what remains, and what should be looked at next. Between the image and the disease lies a layer of clinical reasoning, and the gap between experts and junior physicians is often not about seeing, but about knowing where and what to look for. Key proposals include: reducing repetitive labor and clicks in daily workflow; letting patient information (medical records, lab results, PACS, pathology, prior ultrasound reports, guidelines) flow proactively to the physician with relevance-based filtering; building a longitudinal "life story" for follow-up lesions with side-by-side image comparison; dynamic re-planning of the examination route when a new abnormality is found; experience-adaptive prompt density to avoid alert fatigue; completeness checks before report sign-off; automatic text error-checking; and traceable, guideline-based recommendations instead of black-box outputs. The article also addresses a stepwise implementation path (starting with thyroid ultrasound), the responsibilities of engineers and clinicians in co-design, and the limitations of the vision (operator dependence, generalization across machines, latency, alert fatigue, and responsibility delineation). Its core message: good technology should make physicians stronger, not make them redundant.
Authors
- Kuiyuan Lu (ORCID: https://orcid.org/0009-0008-7933-9897)
Institutions
- Weihai Municipal Hospital (CN)
- Weihai Chest Hospital (CN)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-09-26
- DOI
- https://doi.org/10.5281/zenodo.22978662
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- preprint