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Clinician-AI Collaboration in Endovascular Neurointerventions: In-Vitro User Study of Copilot Assistance Modes for Robotic Navigation Across the Autonomy Spectrum — arXiv:2611.03446

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.03446
License (as archived): CC0-1.0
Archived: 2026-11-03T11:45:24.357Z
SHA-256 of the archived text: babb3a1e8448a386b54eb160a546561691ebbfd5b30c6c63504b018f69c87008
Stable URL of this page: https://metraipolis.com/documento/dc_809affe80306

How to cite

Clinician-AI Collaboration in Endovascular Neurointerventions: In-Vitro User Study of Copilot Assistance Modes for Robotic Navigation Across the Autonomy Spectrum — arXiv:2611.03446 — arXiv (en), https://arxiv.org/abs/2611.03446 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_809affe80306 (Metraipolis archive dc_809affe80306, sha256 babb3a1e8448a386b54eb160a546561691ebbfd5b30c6c63504b018f69c87008)

BibTeX

@misc{dc809affe80306,
  title = {Clinician-AI Collaboration in Endovascular Neurointerventions: In-Vitro User Study of Copilot Assistance Modes for Robotic Navigation Across the Autonomy Spectrum — arXiv:2611.03446},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.03446},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_809affe80306; sha256 babb3a1e8448a386b54eb160a546561691ebbfd5b30c6c63504b018f69c87008},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.03446. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Mechanical thrombectomy (MT) is an effective treatment for acute ischemic stroke, yet access remains limited by the availability of neurointerventional expertise. Telerobotic and AI-assisted systems may help address this challenge, but it remains unclear how clinicians interact with different levels of autonomy and how autonomous navigation systems perform in realistic environments. This work introduces Copilot Assistance Modes (CAMs), a framework for clinician-AI collaboration in robotic endovascular navigation spanning the autonomy spectrum from manual operation to full autonomy. In the first in-vitro user study comparing clinician-AI collaboration strategies, 11 experts and two students evaluated six navigation modes in a patient-specific vascular phantom. Objective performance metrics were combined with subjective assessments, including workload and usability. Autonomous performance was additionally evaluated both in silico and in vitro to investigate sim-to-real transfer. Hands-on manual navigation achieved the strongest overall performance (100% success). While fully autonomous navigation achieved 81% success in simulation, performance dropped to 23% in vitro. In contrast, several clinician-AI shared-autonomy modes achieved success rates of 92-97%, approximately four times higher than full autonomy and comparable to direct robotic control (95%), demonstrating mitigation of sim-to-real domain shift through clinician-AI collaboration. Among the evaluated AI-driven approaches, user-directed task delegation and rotational shared control demonstrated the most favorable balance between performance and clinician acceptance. These findings suggest that effective clinician-AI collaboration and allocation of control authority may currently be more important for reliable robotic neurointerventional navigation than increasing autonomy alone.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv cs.RO, nuovi lavori del giorno).
Categorie arXiv: Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2611.03446 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T11:06:52.325Z dalla pagina https://arxiv.org/list/cs.RO/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).