arXiv:2602.24138v2 Announce Type: replace-cross Abstract: Automated recognition of surgical phases and steps is a fundamental capability for intraoperative decision support, workflow automation, and skill assessment in robotic-assisted surgery. Existing approaches either depend on large-scale annotated surgical datasets or require expensive domain-specific pretraining on thousands of labeled videos, limiting their practical deployability across diverse robotic platforms and clinical environments. In this work, we propose TASOT (Text-Augmented Action Segmentation Optimal Transport), an annotati
The increasing sophistication of AI models and multimodal approaches is enabling new applications for robotics, specifically reducing the reliance on massive, expensive surgical datasets.
This breakthrough addresses a significant barrier to the widespread adoption of AI in surgical robotics, making advanced automation more accessible and scalable across diverse platforms.
The need for extensive, annotated surgical video datasets for AI training is diminished, accelerating the development and deployment of intelligent robotic surgery systems.
- · Surgical robotics companies
- · Healthcare providers
- · AI algorithm developers
- · Patients
- · Companies specializing solely in surgical video annotation services
Surgical robots will gain more autonomous capabilities, enhancing precision and reducing human error.
The cost of developing and deploying advanced surgical AI will decrease, leading to broader accessibility and novel surgical procedures.
This could accelerate the integration of AI across other specialized robotics fields facing similar data scarcity challenges.
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Read at arXiv cs.AI