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The integration of artificial intelligence in robotic surgery: A narrative review.
Zhang, Chi; Hallbeck, M Susan; Salehinejad, Hojjat; Thiels, Cornelius.
Affiliation
  • Zhang C; Department of Surgery, Mayo Clinic Arizona, Phoenix, AZ; Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic Rochester, MN. Electronic address: https://twitter.com/ChiZhang_MD.
  • Hallbeck MS; Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic Rochester, MN; Division of Health Care Delivery Research, Mayo Clinic Rochester, MN; Department of Surgery, Mayo Clinic Rochester, MN.
  • Salehinejad H; Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic Rochester, MN; Division of Health Care Delivery Research, Mayo Clinic Rochester, MN. Electronic address: https://twitter.com/SalehinejadH.
  • Thiels C; Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic Rochester, MN; Department of Surgery, Mayo Clinic Rochester, MN. Electronic address: thiels.cornelius@mayo.edu.
Surgery ; 176(3): 552-557, 2024 Sep.
Article in En | MEDLINE | ID: mdl-38480053
ABSTRACT

BACKGROUND:

The rise of high-definition imaging and robotic surgery has independently been associated with improved postoperative outcomes. However, steep learning curves and finite human cognitive ability limit the facility in imaging interpretation and interaction with the robotic surgery console interfaces. This review presents innovative ways in which artificial intelligence integrates preoperative imaging and surgery to help overcome these limitations and to further advance robotic operations.

METHODS:

PubMed was queried for "artificial intelligence," "machine learning," and "robotic surgery." From the 182 publications in English, a further in-depth review of the cited literature was performed.

RESULTS:

Artificial intelligence boasts efficiency and proclivity for large amounts of unwieldy and unstructured data. Its wide adoption has significant practice-changing implications throughout the perioperative period. Assessment of preoperative imaging can augment preoperative surgeon knowledge by accessing pathology data that have been traditionally only available postoperatively through analysis of preoperative imaging. Intraoperatively, the interaction of artificial intelligence with augmented reality through the dynamic overlay of preoperative anatomical knowledge atop the robotic operative field can outline safe dissection planes, helping surgeons make critical real-time intraoperative decisions. Finally, semi-independent artificial intelligence-assisted robotic operations may one day be performed by artificial intelligence with limited human intervention.

CONCLUSION:

As artificial intelligence has allowed machines to think and problem-solve like humans, it promises further advancement of existing technologies and a revolution of individualized patient care. Further research and ethical precautions are necessary before the full implementation of artificial intelligence in robotic surgery.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Artificial Intelligence / Robotic Surgical Procedures Limits: Humans Language: En Journal: Surgery Year: 2024 Document type: Article Country of publication: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Artificial Intelligence / Robotic Surgical Procedures Limits: Humans Language: En Journal: Surgery Year: 2024 Document type: Article Country of publication: United States