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Robotics for Surgery

Howe, Robert D., Matsuoka, Yoky·1999·Annual Review of Biomedical Engineering

Abstract

▪ Abstract Robotic technology is enhancing surgery through improved precision, stability, and dexterity. In image-guided procedures, robots use magnetic resonance and computed tomography image data to guide instruments to the treatment site. This requires new algorithms and user interfaces for planning procedures; it also requires sensors for registering the patient’s anatomy with the preoperative image data. Minimally invasive procedures use remotely controlled robots that allow the surgeon to work inside the patient’s body without making large incisions. Specialized mechanical designs and sensing technologies are needed to maximize dexterity under these access constraints. Robots have applications in many surgical specialties. In neurosurgery, image-guided robots can biopsy brain lesions with minimal damage to adjacent tissue. In orthopedic surgery, robots are routinely used to shape the femur to precisely fit prosthetic hip joint replacements. Robotic systems are also under development for closed-chest heart bypass, for microsurgical procedures in ophthalmology, and for surgical training and simulation. Although results from initial clinical experience is positive, issues of clinician acceptance, high capital costs, performance validation, and safety remain to be addressed.

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Metadata

Authors
Howe, R. D., & Matsuoka, Y.
Venue
Annual Review of Biomedical Engineering
Published
1999-08
Volume
1
Issue
1
Pages
211-240
ISSN
  • 1523-9829 (ISSN-L)
  • 1545-4274
OpenAlex
W2101637614
Cited by
282
References
0
FWCI
0.35
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Robotics for Surgery. https://app.cochat.ai/papers/10.1146/annurev.bioeng.1.1.211
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Howe, R. D., & Matsuoka, Y. (1999). Robotics for Surgery. Annual Review of Biomedical Engineering. https://doi.org/10.1146/annurev.bioeng.1.1.211

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.