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<p>Ultrasound is one of the most widely used medical imaging modalities in clinical practice. Robot-assisted ultrasound systems (RAUS) offer significant advantages in reducing physician workload and improving imaging standardization. However, the dynamic and complex nature of the human physiological environment severely hinders the transition of RAUS from automation to full autonomy. This paper presents a narrative review of the core research progress of RAUS between 2021 and early 2026. It comprehensively analyzes the current technological landscape of autonomous robotic ultrasound by focusing on three core pillars: perception, planning, and control. By evaluating cutting-edge breakthroughs and existing limitations across these three domains, this paper identifies the key technical bottlenecks that restrict fully autonomous RAUS. Finally, we propose an evolutionary roadmap driven by multimodal fusion, digital twins, planning-control co-design, and embodied artificial intelligence (AI) architectures. This roadmap provides a developmental reference for autonomous robotic ultrasound examinations and also lays a crucial foundation for future autonomous ultrasound-guided surgical interventions.</p>

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Technical challenges in autonomous robotic ultrasound examinations: perception, planning, and control

  • 期刊论文

  • 发表日期：

    2026年5月30日

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  • Dong Guo,
  • Yongde Zhang,
  • Xuequan Huang,
  • Chuang He

摘要

Ultrasound is one of the most widely used medical imaging modalities in clinical practice. Robot-assisted ultrasound systems (RAUS) offer significant advantages in reducing physician workload and improving imaging standardization. However, the dynamic and complex nature of the human physiological environment severely hinders the transition of RAUS from automation to full autonomy. This paper presents a narrative review of the core research progress of RAUS between 2021 and early 2026. It comprehensively analyzes the current technological landscape of autonomous robotic ultrasound by focusing on three core pillars: perception, planning, and control. By evaluating cutting-edge breakthroughs and existing limitations across these three domains, this paper identifies the key technical bottlenecks that restrict fully autonomous RAUS. Finally, we propose an evolutionary roadmap driven by multimodal fusion, digital twins, planning-control co-design, and embodied artificial intelligence (AI) architectures. This roadmap provides a developmental reference for autonomous robotic ultrasound examinations and also lays a crucial foundation for future autonomous ultrasound-guided surgical interventions.