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Artificial intelligence in thoracic surgery: a narrative review

Bellini, Valentina, Valente, Marina, Del Rio, Paolo, Bignami, Elena·2021·Journal of Thoracic Disease
DOI 10.21037/jtd-21-761PMC PMC8743413
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  • AI-driven systems enhance diagnostic accuracy, optimize preoperative planning, and support intraoperative decision-making in complex thoracic procedures such as lung cancer resection and mediastinal interventions.

    “Recent advances in machine learning, deep learning, and computer vision have enabled AI-driven systems to enhance diagnostic accuracy, optimize preoperative planning, and support intraoperative decision-making, particularly in complex procedures such as lung cancer resection and mediastinal interventions [1,2,3].”

    From Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications (2026) · cites this paper as [2]

    The Thoracic lesions management section reports improved lung-nodule detection, and Preoperative evaluation and risk-assessment describes ML aiding risk assessment and therapeutic planning. But Surgical performance and planning says AI use in the operating room is limited and describes intraoperative decision support as a future prospect. It mentions mediastinal resection as a robotic procedure while explicitly distinguishing robotic-assisted surgery from AI, so it does not establish AI support for mediastinal interventions. (LLM, conf=0.87)

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Cited by

Sorted by citations

Authors
Bellini, V., Valente, M., Del Rio, P., & Bignami, E.
Venue
Journal of Thoracic Disease
Publisher
AME Publishing Company
Published
2021-12
Volume
13
Issue
12
Pages
6963-6975
ISSN
  • 2072-1439
  • 2077-6624
DOI
10.21037/jtd-21-761
PMC
PMC8743413
Cited by
41
References
4

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