A Survey of Autonomous Robotic Ultrasound Scanning Systems
Bibliographic record
Abstract
This review investigates recent advancements in autonomous, semi-autonomous, and teleoperated robotic ultrasound systems.Traditional ultrasound imaging depends on manual probe manipulation, which introduces operator variability, physical strain, and limitations in accessibility. To address these challenges, this review investigates recent advancements in autonomous, semi-autonomous, and teleoperated robotic ultrasound systems by analyzing over 60 publications, including key developments from 2022 to 2025. Our survey reveals a growing adoption of cobot-based solutions equipped with 6-DOF force/torque sensors and RGB-D vision systems for precise probe positioning [34], [58], [60]. Notably, several systems now integrate reinforcement learning, image-guided visual servoing, and real-time feedback loops to enable intelligent trajectory planning and adaptive force control [46]–[48]. However, we identify critical gaps in the literature: surface-parallel force and torque components are often ignored in control models, limiting the accuracy of probe orientation and tissue coupling [39], [40]. Furthermore, real-time ultrasound image feedback is rarely used for path optimization, despite its importance in enhancing image quality and diagnostic reliability [38], [50]. This review emphasizes the need for future systems to integrate multi-modal sensing, adaptive control, and real-time image quality assessment to achieve robust, generalizable robotic ultrasound workflows.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".