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Cited by 161Surgical Simulation and TrainingArtificial Intelligence in Healthcare and EducationSoft Robotics and Applications

Artificial intelligence surgery: how do we get to autonomous actions in surgery?

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Gumbs, Andrew A., Frigerio, Isabella, Spolverato, Gaya, Croner, Roland, Illanes, Alfredo, Chouillard, Elie, Elyan, Eyad·2021·Sensors
DOI 10.3390/s21165526PMC PMC8400539
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Abstract

Most surgeons are skeptical as to the feasibility of autonomous actions in surgery. Interestingly, many examples of autonomous actions already exist and have been around for years. Since the beginning of this millennium, the field of artificial intelligence (AI) has grown exponentially with the development of machine learning (ML), deep learning (DL), computer vision (CV) and natural language processing (NLP). All of these facets of AI will be fundamental to the development of more autonomous actions in surgery, unfortunately, only a limited number of surgeons have or seek expertise in this rapidly evolving field. As opposed to AI in medicine, AI surgery (AIS) involves autonomous movements. Fortuitously, as the field of robotics in surgery has improved, more surgeons are becoming interested in technology and the potential of autonomous actions in procedures such as interventional radiology, endoscopy and surgery. The lack of haptics, or the sensation of touch, has hindered the wider adoption of robotics by many surgeons; however, now that the true potential of robotics can be comprehended, the embracing of AI by the surgical community is more important than ever before. Although current complete surgical systems are mainly only examples of tele-manipulation, for surgeons to get to more autonomously functioning robots, haptics is perhaps not the most important aspect. If the goal is for robots to ultimately become more and more independent, perhaps research should not focus on the concept of haptics as it is perceived by humans, and the focus should be on haptics as it is perceived by robots/computers. This article will discuss aspects of ML, DL, CV and NLP as they pertain to the modern practice of surgery, with a focus on current AI issues and advances that will enable us to get to more autonomous actions in surgery. Ultimately, there may be a paradigm shift that needs to occur in the surgical community as more surgeons with expertise in AI may be needed to fully unlock the potential of AIS in a safe, efficacious and timely manner.

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  • Autonomous robots can be particularly useful in situations where human intervention is not practical or safe, such as in hazardous environments or in situations where precision is critical.

    “This can be particularly useful in situations where human intervention is not practical or safe, such as in hazardous environments or in situations where precision is critical [66] .”

    From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [66]

    The Discussion describes autonomous surgical actions and says sensor-assisted needle insertions can become smarter and potentially safer. That lends limited support to their usefulness in precision-sensitive procedures, but the paper does not address hazardous environments or situations where human intervention is impractical or unsafe. The claim extends a surgery-focused discussion to those broader settings. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

  • Advanced manufacturing robots can be equipped with AI and ML algorithms that enable them to operate autonomously.

    “Autonomous Robots: Advanced manufacturing robots can be equipped with AI and ML algorithms that enable them to operate autonomously.”

    From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [66]

    The Introduction says ML and other AI methods are a path toward more autonomous actions in surgical robots. The Discussion describes limited independent surgical actions, but says full autonomy remains a goal. The paper does not address advanced manufacturing robots, so the claim extends its point to a different setting. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Gumbs, A. A., Frigerio, I., Spolverato, G., Croner, R., Illanes, A., Chouillard, E., & Elyan, E.
Affiliations
  • Centre Hospitalier Intercommunal de Poissy (FR)
  • Ospedale P. Pederzoli – Casa di Cura Privata S.p.a. (IT)
  • University of Padua (IT)
  • University Hospital Magdeburg (DE)
  • INKA–Innovation Laboratory for Image Guided Therapy, Medical Faculty, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany
  • Robert Gordon University (GB)
Corresponding author
Gumbs, Andrew A.
ORCID
Venue
Sensors
Publisher
MDPI AG
Published
2021-08-17
Volume
21
Issue
16
Pages
5526
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s21165526
PMC
PMC8400539
OpenAlex
W3193396633
Cited by
161
References
14
Citations by year
2026: 16 · 2025: 27 · 2024: 45 · 2023: 45 · 2022: 25 · 2021: 3
FWCI
7.00
Type
journal article
Language
en
Open access
Licence
Locations
  • Sensors (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Open Access Institutional Repository at Robert Gordon University (Robert Gordon University) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Padua Research Archive (University of Padova) (submittedVersion, cc-by)
Topics
  • Health Sciences › Medicine › Surgery › Surgical Simulation and Training
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Engineering › Biomedical Engineering › Soft Robotics and Applications
Keywords
autonomous surgical robots, machine learning, deep learning, computer vision, natural language processing, haptics, teleoperation, surgical robotics, interventional radiology, endoscopy
MeSH terms
Machine Learning, Artificial Intelligence, Endoscopy, Humans, Natural Language Processing, Robotics

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