Evidence map›Paper›PMID 38671232›Full record

ReviewNPJ digital medicine2024

Levels of autonomy in FDA-cleared surgical robots: a systematic review.

Audrey Lee, Turner S Baker, Joshua B Bederson, Benjamin I Rapoport

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
61citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

61 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
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  4. Trial
  5. Review
  6. An Expertise Transfer Framework For Autonomous Surgical Assistance.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
  7. Assessing selective interaction for reliable and trustworthy robotic surgical assistance.International journal of computer assisted radiology and surgery · 2026
    Article
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1 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Audrey LeeDepartment of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York, USA.ORCID http://orcid.org/0000-0002-4830-5781
Turner S BakerDepartment of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Joshua B BedersonDepartment of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Benjamin I RapoportDepartment of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York, USA. benjamin.rapoport@mountsinai.org.ORCID http://orcid.org/0000-0002-0049-6419

Funding

MOUNT SINAI MEDICAL SCIENTIST TRAINING PROGRAMT32GM007280 · NIGMS · MOUNT SINAI SCHOOL OF MEDICINE OF NYU · PI BARON, MARGARET H · 1985 to 2021
$22.8M
NIGMS NIH HHS T32 GM007280
6 · The paper itself

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.

Identifiers

PMID38671232
PMCPMC11053143

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.