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The future of robotic surgery in the age of artificial intelligence

Nature Reviews Urology (2026) Cite this article

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Abstract

Patient outcomes after robotic surgery vary widely, often reflecting differences in surgical performance. Artificial intelligence (AI) offers new ways to address this variability, with applications spanning automated skills assessment and feedback, intraoperative guidance and autonomous surgery. The most credible short-range advances of AI in this space consist in generating assistive systems that enhance perception, anticipate risks and standardize feedback while remaining under surgeon control. Results from early studies suggest that AI can influence decision-making, reduce errors and shorten learning curves, particularly in areas such as augmented navigation, anatomy recognition, error detection and telesurgery support. Long-term directions include emerging vision–language–action interfaces capable of programming task-specific support through natural language. In addition to technical performance, translation of AI into clinical practice will require robust datasets, systems designed around human users, regulatory alignment and clear accountability. Ultimately, the measure of surgical AI will be patient outcomes, including reduced complications, fast proficiency acquisition and improved outcome consistency across diverse settings.

Key points

  • Artificial intelligence (AI) offers a strategy to improve consistency in surgical performance by enabling objective, reproducible and scalable skill assessment from intraoperative video and kinematic data, mitigating the subjectivity and labour intensiveness of traditional expert-rater feedback. Realizing this potential will require moving from proxy metrics and discrete skill categories towards continuous, outcome-anchored measures of competence.

  • Intraoperative assistive systems, including augmented-reality 3D models, anatomy recognition, early bleeding detection and anticipatory coaching, have already shown measurable benefits in procedures such as robot-assisted radical prostatectomy and partial nephrectomy, reducing positive surgical margins, blood loss and ischaemia times while keeping the surgeon firmly in control.

  • Autonomous surgery remains speciality specific and early stage. In rigid-anatomy fields (orthopaedics, ophthalmology and radiosurgery), level 2–3 systems are already in routine clinical use, whereas in soft-tissue surgery, level 4 demonstrations such as Smart Tissue Autonomous Robot (STAR) and Surgical Robot Transformer-Hierarchy (SRT-H) are currently confined to ex vivo and preclinical animal models.

  • Four complementary learning paradigms — reinforcement learning, imitation learning, hybrid approaches and vision–language–action (VLA) models — outline an incremental, testable path towards autonomy, with VLA being a long-term prospect for a programmable, language-driven form of surgical autonomy portable across subtasks and anatomies.

  • Clinical translation of surgical AI requires diverse, multicentre datasets, prospective validation according to AI-specific reporting standards (DECIDE-AI, TRIPOD-AI, SPIRIT-AI and CONSORT-AI), regulatory alignment and clear lines of accountability, alongside privacy-preserving and federated strategies for handling sensitive patient data.

  • The ultimate measure of surgical AI will not be technical performance, but patient-relevant outcomes such as reduced complications, improved proficiency acquisition and increased consistency of results across diverse health-care settings.

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Fig. 1: Pathways towards clinically integrated AI in robotic surgery.
The alternative text for this image may have been generated using AI.
Fig. 2: Directions of AI in robotic surgery.
The alternative text for this image may have been generated using AI.

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Acknowledgements

The research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under award numbers R01CA251579, R01CA273031 and R01CA298988. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The manuscript was edited for grammar and structure using an advanced language model.

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  1. These authors contributed equally: Ludovica Cella, Jasmine Lin.

Authors and Affiliations

  1. Department of Biochemical Science, Humanitas University, Milan, Italy

    Ludovica Cella

  2. Department of Urology, Humanitas Clinical and Research Institute IRCCS, Milan, Italy

    Ludovica Cella

  3. Department of Urology, Cedars-Sinai Medical Center, Los Angeles, CA, USA

    Jasmine Lin & Andrew J. Hung

  4. Catherine and Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA

    Mitchell G. Goldenberg

  5. Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA

    Andrew J. Hung

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L.C. and J.L. researched data for the article. L.C. and J.L. contributed substantially to discussion of the content. L.C. and J.L. wrote the article. A.J.H. and M.G.G. reviewed and/or edited the manuscript before submission.

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Cella, L., Lin, J., Goldenberg, M.G. et al. The future of robotic surgery in the age of artificial intelligence. Nat Rev Urol (2026). https://doi.org/10.1038/s41585-026-01153-8

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