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Artificial Intelligence in Thoracic Surgery: A Review Bridging Innovation and Clinical Practice for the Next Generation of Surgical Care

Leivaditis, Vasileios, Maniatopoulos, Andreas Antonios, Lausberg, Henning, Mulita, Francesk, Papatriantafyllou, Athanasios, Liolis, Elias, Beltsios, Eleftherios, Adamou, Antonis +2 more·2025·Journal of Clinical Medicine
DOI 10.3390/jcm14082729PMC PMC12027631
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Abstract

Background: Artificial intelligence (AI) is rapidly transforming thoracic surgery by enhancing diagnostic accuracy, surgical precision, intraoperative guidance, and postoperative management. AI-driven technologies, including machine learning (ML), deep learning, computer vision, and robotic-assisted surgery, have the potential to optimize clinical workflows and improve patient outcomes. However, challenges such as data integration, ethical concerns, and regulatory barriers must be addressed to ensure AI’s safe and effective implementation. This review aims to analyze the current applications, benefits, limitations, and future directions of AI in thoracic surgery. Methods: This review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive literature search was performed using PubMed, Scopus, Web of Science, and Cochrane Library for studies published up to January 2025. Relevant articles were selected based on predefined inclusion and exclusion criteria, focusing on AI applications in thoracic surgery, including diagnostics, robotic-assisted surgery, intraoperative guidance, and postoperative care. A risk of bias assessment was conducted using the Cochrane Risk of Bias Tool and ROBINS-I for non-randomized studies. Results: Out of 279 identified studies, 36 met the inclusion criteria for qualitative synthesis, highlighting AI’s growing role in diagnostic accuracy, surgical precision, intraoperative guidance, and postoperative care in thoracic surgery. AI-driven imaging analysis and radiomics have improved pulmonary nodule detection, lung cancer classification, and lymph node metastasis prediction, while robotic-assisted thoracic surgery (RATS) has enhanced surgical accuracy, reduced operative times, and improved recovery rates. Intraoperatively, AI-powered image-guided navigation, augmented reality (AR), and real-time decision-support systems have optimized surgical planning and safety. Postoperatively, AI-driven predictive models and wearable monitoring devices have enabled early complication detection and improved patient follow-up. However, challenges remain, including algorithmic biases, a lack of multicenter validation, high implementation costs, and ethical concerns regarding data security and clinical accountability. Despite these limitations, AI has shown significant potential to enhance surgical outcomes, requiring further research and standardized validation for widespread adoption. Conclusions: AI is poised to revolutionize thoracic surgery by enhancing decision-making, improving patient outcomes, and optimizing surgical workflows. However, widespread adoption requires addressing key limitations through multicenter validation studies, standardized AI frameworks, and ethical AI governance. Future research should focus on digital twin technology, federated learning, and explainable AI (XAI) to improve AI interpretability, reliability, and accessibility. With continued advancements and responsible integration, AI will play a pivotal role in shaping the next generation of precision thoracic surgery.

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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 [1]

    Sections 3.1 and 3.8 support AI-enhanced diagnostic accuracy, thoracic surgical planning, and intraoperative decision support, including for lung cancer surgery. Section 3.1 also describes AI for mediastinal staging, but the discussion of mediastinal surgery in section 3.8 concerns robotic techniques rather than AI-driven planning or intraoperative decision-making for those procedures. Page numbers are not available in our copy. (LLM, conf=0.83)

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Cited by

Sorted by citations

Authors
Leivaditis, V., Maniatopoulos, A. A., Lausberg, H., Mulita, F., Papatriantafyllou, A., Liolis, E., Beltsios, E., Adamou, A., Kontodimopoulos, N., & Dahm, M.
Affiliations
  • Department of Cardiothoracic and Vascular Surgery, Westpfalz Klinikum, 67655 Kaiserslautern, Germany
  • Department of Electrical and Computer Engineering, Democritus University of Thrace, 67100 Xanthi, Greece
  • Department of General Surgery, General Hospital of Eastern Achaia—Unit of Aigio, 25100 Aigio, Greece
  • Department of Oncology, General University Hospital of Patras, 26504 Patras, Greece
  • Department of Anesthesiology and Intensive Care, Hannover Medical School, 30625 Hannover, Germany
  • Institute of Diagnostic and Interventional Neuroradiology, Hannover Medical School, 30625 Hannover, Germany
  • Department of Economics and Sustainable Development, Harokopio University, 17676 Athens, Greece
ORCID
Venue
Journal of Clinical Medicine
Publisher
MDPI AG
Published
2025-04-16
Volume
14
Issue
8
Pages
2729
ISSN
2077-0383
DOI
10.3390/jcm14082729
PMC
PMC12027631
Cited by
71
References
6
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)

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