1. Artificial Intelligence, Machine Learning, and Surgical Science: Reality Versus Hype
- Author
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Majed El Hechi, Gary An, Georgios Tsoulfas, Lydia R. Maurer, Haytham M.A. Kaafarani, Thomas M. Ward, and Mohamad El Moheb
- Subjects
business.industry ,Clinical Decision-Making ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Machine learning ,computer.software_genre ,Risk Assessment ,Machine Learning ,03 medical and health sciences ,0302 clinical medicine ,Emergency surgery ,General Surgery ,030220 oncology & carcinogenesis ,Decision aids ,Humans ,030211 gastroenterology & hepatology ,Surgery ,Artificial intelligence ,business ,computer - Abstract
Artificial intelligence (AI) has made increasing inroads in clinical medicine. In surgery, machine learning-based algorithms are being studied for use as decision aids in risk prediction and even for intraoperative applications, including image recognition and video analysis. While AI has great promise in surgery, these algorithms come with a series of potential pitfalls that cannot be ignored as hospital systems and surgeons consider implementing these technologies. The aim of this review is to discuss the progress, promise, and pitfalls of AI in surgery.
- Published
- 2021
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