Toward Clinical Applications of Intelligent Robotic Ultrasound Systems
The Robotic Ultrasound System (RUSS) has the potential to transform medical imaging by addressing limitations such as operator dependency, diagnostic variability, and reproducibility in traditional ultrasound (US) examination. Despite rapid technological advancements, a substantial gap remains between RUSS research progress and clinical adoption. This review examined the clinical roles and engineering advances of RUSS, identifying key barriers to translation. Clinically, it evaluated the current applications of RUSS in supporting US procedures, while from an engineering standpoint, it summarized recent innovations and remaining technical challenges. This review examined the current state-of-the-art RUSS technologies, categorizing them based on diverse organ-specific applications while also analyzing their core functional capabilities. This review revealed a focus disparity: while abdominal US is the most commonly used in clinical practice, vascular-targeted RUSS dominates current research. It also highlighted a misalignment between research priorities and actual clinical tasks. Current studies predominantly focused on autonomous scanning and imaging, with limited attention to downstream tasks such as disease diagnosis and analysis. Building on these observations, it identified critical challenges and future trends in RUSS development. This work provides a foundation for future research, fostering collaboration between clinicians and engineers to accelerate the translation of next-generation RUSS from bench to bedside.
Authors
- Hongen Liao (ORCID: https://orcid.org/0000-0003-3847-9347)
- Guochen Ning (ORCID: https://orcid.org/0000-0003-0282-7303)
- Fang Chen (ORCID: https://orcid.org/0000-0003-1926-3807)
- Zhongliang Jiang (ORCID: https://orcid.org/0000-0001-7461-2200)
- Yan Kang (ORCID: https://orcid.org/0000-0002-4532-5744)
- Jianwen Luo (ORCID: https://orcid.org/0000-0001-9215-5568)
- Taiyu Han
- Hanying Liang (ORCID: https://orcid.org/0000-0001-7197-7191)
- Zihan Li (ORCID: https://orcid.org/0000-0001-8950-6515)
Institutions
- Shenzhen University (CN)
- Shanghai Jiao Tong University (CN)
- Shenzhen Technology University (CN)
- Beijing Tsinghua Chang Gung Hospital (CN)
- Technical University of Munich (DE)
- Tsinghua University (CN)
Publication Details
- Journal
- IEEE Reviews in Biomedical Engineering
- Published
- 2025-10-02
- DOI
- https://doi.org/10.1109/rbme.2025.3610605
- Primary Topic
- Soft Robotics and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00