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Artificial intelligence in thoracic surgery: past, present, perspective and limits

Etienne, Harry, Hamdi, Sarah, Le Roux, Marielle, Camuset, Juliette, Khalife-Hocquemiller, Theresa, Giol, Mihaela, Debrosse, Denis, Assouad, Jalal·2020·European Respiratory Review
DOI 10.1183/16000617.0010-2020PMC PMC9488625
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Abstract

Artificial intelligence (AI) technology is becoming prevalent in many areas of everyday life. The healthcare industry is concerned by it even though its widespread use is still limited. Thoracic surgeons should be aware of the new opportunities that could affect their daily practice, by direct use of AI technology or indirect use via related medical fields (radiology, pathology and respiratory medicine). The objective of this article is to review applications of AI related to thoracic surgery and discuss the limits of its application in the European Union. Key aspects of AI will be developed through clinical pathways, beginning with diagnostics for lung cancer, a prognostic-aided programme for decision making, then robotic surgery, and finishing with the limitations of AI, the legal and ethical issues relevant to medicine. It is important for physicians and surgeons to have a basic knowledge of AI to understand how it impacts healthcare, and to consider ways in which they may interact with this technology. Indeed, synergy across related medical specialties and synergistic relationships between machines and surgeons will likely accelerate the capabilities of AI in augmenting surgical care.

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  • AI-powered imaging analysis and radiomics have improved detection and classification of pulmonary nodules, prediction of lymph node metastasis, and integration of molecular and radiomic features for non-invasive stratification.

    “AI-powered imaging analysis and radiomics have improved detection and classification of pulmonary nodules, prediction of lymph node metastasis, and integration of molecular and radiomic features for non-invasive stratification [4,5,6].”

    From Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications (2026) · cites this paper as [5]

    The “Diagnostic for lung cancer” section reports AI-assisted detection and benign-versus-malignant classification of pulmonary nodules, including improved detection performance. It does not report prediction of lymph-node metastasis or integration of molecular and radiomic features for non-invasive stratification; searches for those terms found no supporting passage. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

  • AI, particularly the Naive Bayes Classifier, is highly effective for predictive modeling.

    “Their findings support Chang et al.’s observation that AI, particularly the Naïve Bayes Classifier, is highly effective for predictive modeling.”

    From Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications (2026) · cites this paper as [5]

    The “Prognostic-aided programme for decision-making” section reports promising predictive results for AI in thoracic surgery, including neural-network models. Our copy does not identify a Naïve Bayes Classifier or support the claim that it is particularly effective; searches for “Naive Bayes Classifier” and “Chang predictive modeling Bayes” found no such passage. Page numbers were unavailable. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • Etienne et al. reported successful applications of AI in predicting cardiorespiratory complications and postoperative prognosis in non-small-cell lung cancer patients.

    “Etienne et al. [5] reported successful applications of AI in predicting cardiorespiratory complications and postoperative prognosis in non-small-cell lung cancer patients.”

    From Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications (2026) · cites this paper as [5]

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Cited by

Sorted by citations

Authors
Etienne, H., Hamdi, S., Le Roux, M., Camuset, J., Khalife-Hocquemiller, T., Giol, M., Debrosse, D., & Assouad, J.
Venue
European Respiratory Review
Publisher
European Respiratory Society (ERS)
Published
2020-08-18
Volume
29
Issue
157
Pages
200010
ISSN
  • 0905-9180
  • 1600-0617
DOI
10.1183/16000617.0010-2020
PMC
PMC9488625
Cited by
48
References
8
Language
en
Licence
creativecommons.org/licenses/by-nc/4.0/ (vor)

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