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Peer-reviewedCC BYDentistry Review

Surgeon–robot coupling through motion and force analysis in oral and maxillofacial robotic surgery

  • Syed Immad (Immad) Shah1
  • Marij W. Janjua1
  • Olivia Metzger1
  • Tal Sahar1
  • Makesh Srinivasan2
  • Rui Li3
  • Thomas G. Wiedemann4
  • Young K. Kim5
5 affiliations
  1. 1Dept. of Cariology and Comprehensive Care, NYU College of Dentistry, NY, NY, United States
  2. 2Research Technology Services GenAI, NYU IT, NY, NY, United States
  3. 3Dept. of Mechanical and Aerospace Engineering, NYU Tandon School of Engineering, NY, NY, United States
  4. 4Dept. of Oral & Maxillofacial Surgery, NYU College of Dentistry, NY, NY, United States; Corresponding author at: Department of Oral & Maxillofacial Surgery, New York University College of Dentistry, New York, NY, United States.
  5. 5Dept. of Prosthodontics, NYU College of Dentistry, NY, NY, United States; Corresponding author at: Department of Prosthodontics, New York University College of Dentistry, New York, NY, United States.
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Abstract

Surgical performance has traditionally been evaluated through clinical outcomes, while technical execution—reflected in instrument motion and applied force, currently merged through the clinician’s dexterity—remains largely unquantified. Artificial intelligence–integrated robotic platforms may provide objective measures of biomechanical and motion-related variables. This narrative review synthesizes evidence on instrument movement and force control during robot-assisted surgery and considers how these measures characterize surgeon–robot interaction. It uses surgeon–robot coupling to organize these variables while recognizing that no validated composite metric currently exists. The reviewed evidence suggests that AI-integrated robotic platforms may support motion stability, positional accuracy, and process efficiency while limiting excessive force application throughout complex interventions. Despite limitations such as force-feedback latency and limited contextual integration, AI-driven robotic technologies show potential to support better clinical outcomes, procedural safety, and technical training across precision-dependent operative disciplines, with direct dental evidence concentrated primarily in robotic implant surgery and broader maxillofacial applications remaining limited.

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Cite

BibTeXRIS
BibTeX
@article{shah2026surgeonrobot,
  title = {{Surgeon–robot coupling through motion and force analysis in oral and maxillofacial robotic surgery}},
  author = {Syed Immad (Immad) Shah and Marij W. Janjua and Olivia Metzger and Tal Sahar and Makesh Srinivasan and Rui Li and Thomas G. Wiedemann and Young K. Kim},
  journal = {Dentistry Review},
  year = {2026},
  month = oct,
  doi = {10.1016/j.dentre.2026.100440},
  url = {https://doi.org/10.1016/j.dentre.2026.100440},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Surgeon–robot coupling through motion and force analysis in oral and maxillofacial robotic surgery
AU  - Syed Immad (Immad) Shah
AU  - Marij W. Janjua
AU  - Olivia Metzger
AU  - Tal Sahar
AU  - Makesh Srinivasan
AU  - Rui Li
AU  - Thomas G. Wiedemann
AU  - Young K. Kim
PY  - 2026
DA  - 2026/10/30
JO  - Dentistry Review
DO  - 10.1016/j.dentre.2026.100440
UR  - https://doi.org/10.1016/j.dentre.2026.100440
AB  - Surgical performance has traditionally been evaluated through clinical outcomes, while technical execution—reflected in instrument motion and applied force, currently merged through the clinician’s dexterity—remains largely unquantified. Artificial intelligence–integrated robotic platforms may provide objective measures of biomechanical and motion-related variables. This narrative review synthesizes evidence on instrument movement and force control during robot-assisted surgery and considers how these measures characterize surgeon–robot interaction. It uses surgeon–robot coupling to organize these variables while recognizing that no validated composite metric currently exists. The reviewed evidence suggests that AI-integrated robotic platforms may support motion stability, positional accuracy, and process efficiency while limiting excessive force application throughout complex interventions. Despite limitations such as force-feedback latency and limited contextual integration, AI-driven robotic technologies show potential to support better clinical outcomes, procedural safety, and technical training across precision-dependent operative disciplines, with direct dental evidence concentrated primarily in robotic implant surgery and broader maxillofacial applications remaining limited.
SN  - 2772-5596
ER  -