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Robotic Ultrasound Imaging: A Comprehensive Review of Historical Evolution, Current State-of-the-Art, and Future Perspectives.

August 24, 2026pubmed logopapers

Authors

Chapala S,Gibson J,Chinniah P,Chandrashekhara SH,Kiyawat V,Parmar Y,Botchu R

Affiliations (6)

  • Department of Radiology, AIG Hospital, Hyderabad, India.
  • University of Birmingham, College of Medical and Dental Sciences, Birmingham, UK.
  • Emergency Radiology, Sunnybrook Health Centre, Toronto, Canada.
  • Department of Diagnostic and Interventional Onco-Radiology, All India Institute of Medical Sciences (AIIMS), New Delhi, India.
  • Department of Musculoskeletal Radiology, Royal Orthopaedic Hospital, Birmingham, UK.
  • Department of Physiotherapy, Board of Control for Cricket in India (BCCI), Mumbai, India.

Abstract

Ultrasound imaging is an indispensable diagnostic tool, yet its profound reliance on operator expertise inherently restricts its reproducibility and global accessibility. Robotic ultrasound systems (RUSS) have evolved over the past 2 decades to mitigate these limitations by mechanically decoupling the human operator from the patient. This comprehensive review examines the historical trajectory of medical ultrasonography and robotics, highlighting their convergence into modern RUSS. We detail the taxonomies of robotic autonomy and evaluate the clinical impact of teleoperated systems (telesonography), which increasingly leverage ultra-low-latency 5G networks to project diagnostic expertise globally. Furthermore, we dissect the enabling hardware and control algorithms essential for autonomous acquisition, including compliant force control, probe orientation optimization, and dynamic path generation. The contemporary integration of artificial intelligence (AI), particularly deep learning, physics-inspired neural networks, and reinforcement learning, has catalyzed a paradigm shift toward fully autonomous systems capable of semantic reasoning, motion-aware imaging, and deformation compensation. This review explores emerging frontiers, such as soft robotics, wearable ultrasound patches, and large language model (LLM) graph planners, while addressing the critical regulatory and ethical frameworks required for the future clinical translation of intelligent robotic sonographers.

Topics

Journal ArticleReview

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