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Artificial Intelligence in Surgery: Promises and Perils

Hashimoto, Daniel, A., MD, MS*; Rosman, Guy, PhD; Rus, Daniela, PhD; Meireles, Ozanan, R., MD, FACS*

doi: 10.1097/SLA.0000000000002693
REVIEW PAPER

Objective: The aim of this review was to summarize major topics in artificial intelligence (AI), including their applications and limitations in surgery. This paper reviews the key capabilities of AI to help surgeons understand and critically evaluate new AI applications and to contribute to new developments.

Summary Background Data: AI is composed of various subfields that each provide potential solutions to clinical problems. Each of the core subfields of AI reviewed in this piece has also been used in other industries such as the autonomous car, social networks, and deep learning computers.

Methods: A review of AI papers across computer science, statistics, and medical sources was conducted to identify key concepts and techniques within AI that are driving innovation across industries, including surgery. Limitations and challenges of working with AI were also reviewed.

Results: Four main subfields of AI were defined: (1) machine learning, (2) artificial neural networks, (3) natural language processing, and (4) computer vision. Their current and future applications to surgical practice were introduced, including big data analytics and clinical decision support systems. The implications of AI for surgeons and the role of surgeons in advancing the technology to optimize clinical effectiveness were discussed.

Conclusions: Surgeons are well positioned to help integrate AI into modern practice. Surgeons should partner with data scientists to capture data across phases of care and to provide clinical context, for AI has the potential to revolutionize the way surgery is taught and practiced with the promise of a future optimized for the highest quality patient care.

*Department of Surgery, Massachusetts General Hospital, Boston, MA

Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, MA.

Reprints: Daniel A. Hashimoto, MD, MS, Department of Surgery, Massachusetts General Hospital, 55 Fruit Street, GRB 425, Boston, MA 02114. E-mail: dahashimoto@mgh.harvard.edu.

Disclosures: Daniel Hashimoto is financially supported by NIH grant T32DK007754-16A1 and the Massachusetts General Hospital Edward D. Churchill Fellowship. Drs. Ozanan Meireles and Daniel Hashimoto have grant funding from the Natural Orifice Surgery Consortium for Assessment and Research (NOSCAR) for research related to computer vision in endoscopic surgery. Guy Rosman and Daniela Rus are grant funded by Toyota Research Institute for research on autonomous vehicles. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH, NOSCAR, or TRI.

The authors report no conflict of interests.

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