Artificial intelligence in cardiothoracic surgery
- PMID: 32989966
- PMCID: PMC7959017
- DOI: 10.23736/S0026-4725.20.05235-4
Artificial intelligence in cardiothoracic surgery
Abstract
The tremendous and rapid technological advances that humans have achieved in the last decade have definitely impacted how surgical tasks are performed in the operating room (OR). As a high-tech work environment, the contemporary OR has incorporated novel computational systems into the clinical workflow, aiming to optimize processes and support the surgical team. Artificial intelligence (AI) is increasingly important for surgical decision making to help address diverse sources of information, such as patient risk factors, anatomy, disease natural history, patient values and cost, and assist surgeons and patients to make better predictions regarding the consequences of surgical decisions. In this review, we discuss the current initiatives that are using AI in cardiothoracic surgery and surgical care in general. We also address the future of AI and how high-tech ORs will leverage human-machine teaming to optimize performance and enhance patient safety.
Conflict of interest statement
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References
-
- Joshi AV. Essential concepts in artificial intelligence and machine learning. Machine Learning and Artificial Intelligence. Redmond, WA: Springer; 2020. p.9–20
-
- Kilic A Artificial Intelligence and Machine Learning in Cardiovascular Health Care. Ann Thorac Surg 2020;109:1323–9. - PubMed
-
- Gleichgerrcht E, Munsell B, Bhatia S, Vandergrift WA 3rd, Rorden C, McDonald C, et al. Deep learning applied to whole-brain connectome to determine seizure control after epilepsy surgery. Epilepsia 2018;59:1643–54. - PubMed
-
- Newmarker C Digital Surgery touts artificial intelligence for the operating room ∣ Medical Design and Outsourcing. Medical Design and Outsourcing; 2018. [Internet]. Available from: https://www.medicaldesignandoutsourcing.com/digital-surgery-touts-artifi... [cited 2020, Jul 16].
-
- Wijnberge M, Geerts BF, Hol L, Lemmers N, Mulder MP, Berge P, et al. Effect of a Machine Learning-Derived Early Warning System for Intraoperative Hypotension vs Standard Care on Depth and Duration of Intraoperative Hypotension During Elective Noncardiac Surgery: The HYPE Randomized Clinical Trial. JAMA 2020;323:l052–60. - PMC - PubMed
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