Levels of autonomy in FDA-cleared surgical robots: a systematic review
Abstract
The integration of robotics in surgery has increased over the past decade, and advances in the autonomous capabilities of surgical robots have paralleled that of assistive and industrial robots. However, classification and regulatory frameworks have not kept pace with the increasing autonomy of surgical robots. There is a need to modernize our classification to understand technological trends and prepare to regulate and streamline surgical practice around these robotic systems. We present a systematic review of all surgical robots cleared by the United States Food and Drug Administration (FDA) from 2015 to 2023, utilizing a classification system that we call Levels of Autonomy in Surgical Robotics (LASR) to categorize each robot’s decision-making and action-taking abilities from Level 1 (Robot Assistance) to Level 5 (Full Autonomy). We searched the 510(k), De Novo, and AccessGUDID databases in December 2023 and included all medical devices fitting our definition of a surgical robot. 37,981 records were screened to identify 49 surgical robots. Most surgical robots were at Level 1 (86%) and some reached Level 3 (Conditional Autonomy) (6%). 2 surgical robots were recognized by the FDA to have machine learning-enabled capabilities, while more were reported to have these capabilities in their marketing materials. Most surgical robots were introduced via the 510(k) pathway, but a growing number via the De Novo pathway. This review highlights trends toward greater autonomy in surgical robotics. Implementing regulatory frameworks that acknowledge varying levels of autonomy in surgical robots may help ensure their safe and effective integration into surgical practice.
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Current surgical robots in thoracic surgery operate predominantly at Level 1 (Robot Assistance), with 86% of FDA-cleared surgical robots functioning at this level.
“Current surgical robots in thoracic surgery operate predominantly at Level 1 (Robot Assistance), with 86% of FDA-cleared surgical robots functioning at this level.”
From Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications (2026) · cites this paper as [54]
A small proportion of systems (6%) have achieved Level 3 (Conditional Autonomy), capable of executing specific tasks such as suturing or tissue manipulation autonomously under surgeon supervision.
“A small proportion of systems (6%) have achieved Level 3 (Conditional Autonomy), capable of executing specific tasks such as suturing or tissue manipulation autonomously under surgeon supervision.”
From Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications (2026) · cites this paper as [54]
No clinical investigations of autonomous surgical AI on human patients have been reported, with all studies remaining at preclinical stages using in silico simulations, ex vivo models, or animal experiments.
“Critically, no clinical investigations of autonomous surgical AI on human patients have been reported, with all studies remaining at preclinical stages using in silico simulations, ex vivo models, or animal experiments [54].”
From Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications (2026) · cites this paper as [54]
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The PRISMA 2020 statement: An updated guideline for reporting systematic reviews
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., et al. (2021) · BMJ · doi:10.1136/bmj.n71 · Cited by 108,283 · see citing works
Bias in AI-based models for medical applications: challenges and mitigation strategies
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“Machine learning algorithms may propagate biases that exist within training data 20 .”
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Research works printed but not linked: 12
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Petrella, F., & Rizzo, S. (2026) · Cancers · doi:10.3390/cancers18020246 · Cited by 10 · see citing works
- Authors
- Lee, A., Baker, T. S., Bederson, J. B., & Rapoport, B. I.
- Venue
- npj Digital Medicine
- Published
- 2024-04-26
- Volume
- 7
- Issue
- 1
- ISSN
- 2398-6352
- PMC
- PMC11053143
- Cited by
- 157
- References
- 2
- Language
- en
- Funders
- Medical Scientist Training Program (MSTP) at the Icahn School of Medicine at Mount Sinai
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