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REVIEW ARTICLE

Multimodal perception and control in intraoperative neurosurgical robotics: Integrating data for precision decision-makings

Faiqa Ijaz Khan1 ,  Syed Haider Hassan2 ,  Khawar Anwar1 ,  Haseeb Mehmood Qadri1* ,  Nasruddin Ansari1,3 ,  Sundas Irshad1 ,  Arham Amir Khawaja4 ,  Asif Bashir1
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1 Department of Neurosurgery Unit 1, Punjab Institute of Neurosciences, Lahore, Punjab, Pakistan
2 Department Neurological Surgery, Faculty of Surgery, University of Wisconsin–Madison, Madison, Wisconsin, United States of America
3 Department of Neurosurgery Unit 3, Punjab Institute of Neurosciences, Lahore, Punjab, Pakistan
4 Department of General Surgery, Shaikh Zayed Medical Complex, Lahore, Punjab, Pakistan
EIR, 025510013 https://doi.org/10.36922/EIR025510013
Received: 16 December 2025 | Revised: 11 February 2026 | Accepted: 28 February 2026 | Published online: 3 April 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Neurosurgery demands meticulous surgical planning and decision-making. To minimize the impact of human error during procedural implementation, intraoperative robotic systems have become vital for aiding surgeons and optimizing patient care and outcomes. Current systems, however, use only a few modalities, with some critical functions missing in each system. A literature search was conducted, incorporating the latest evidence on multimodal perception and neurosurgical robotics. Relevant articles addressing robotic systems, multimodal sensing, data integration, and control strategies for precision decision-making were identified to summarize current advances and challenges in the field. The cornerstone of multimodal perception-driven decision-making is a robotic system capable of sensing all inputs—analogous to human vision, touch, and hearing—and integrating them into a meaningful plan of action. This approach would transform the role of robotics from an assistive role to a fully functioning entity capable of operating independently when supplied with accurate inputs and appropriate supervisory parameters. It also requires the robot to not only be aware of the operating field but also to interpret, decide, adapt, and implement actions with minimal error. However, robotic systems that encompass these modalities face latency issues in real-time temporal or spatial coordination. A robot-assisted operating room also requires seamless integration of actuator and sensor systems. Additionally, in neurosurgical robotics, control strategies that leverage multimodal input are paramount. Situations may arise—particularly in unstructured environments—where real-time human decision-making remains essential. Certain challenges arise from implementing multimodal perception, including technical, regulatory, and ethical concerns. Additionally, more than two-thirds of the global population resides in low- and lower–middle-income countries, where cost, skill availability, and access to advanced techniques pose significant barriers. Despite these constraints, sensor data fusion and digital twins offer a promising path forward for neurosurgical planning and intraoperative updates. Overall, this highlights both the potential and promise of multimodal robotic systems, while emphasizing the need for clinical–technical collaboration, which will ultimately pave the way for intelligent robotic neurosurgery.

Keywords
Robotics
Neurosurgery
Haptic technology
Artificial intelligence
Augmented Reality
Multimodal imaging
Funding
None.
Conflict of interest
The authors declare that they have no competing interests.
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