Current applications of artificial intelligence for intraoperative decision support in surgery

Abstract

Research into medical artificial intelligence (AI) has made significant advances in recent years, including surgical applications. This scoping review investigated AI-based decision support systems targeted at the intraoperative phase of surgery and found a wide range of technological approaches applied across several surgical specialties. Within the twenty-one (n=21) included papers, three main categories of motivations were identified for developing such technologies: (1) augmenting the information available to surgeons, (2) accelerating intraoperative pathology, and (3) recommending surgical steps. While many of the proposals hold promise for improving patient outcomes, important methodological shortcomings were observed in most of the reviewed papers that made it difficult to assess the clinical significance of the reported performance statistics. Despite limitations, the current state of this field suggests that a number of opportunities exist for future researchers and clinicians to work on AI for surgical decision support with exciting implications for improving surgical care.

Keywords

artificial intelligence / decision support / clinical decision support systems / intraoperative / deep learning / computer vision / machine learning / surgery

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Compliance with ethics guidelines

Allison J. Navarrete-Welton has received research support from Olympus Corporation for projects outside of this paper. Daniel A. Hashimoto is an independent consultant for Verily Life Sciences and the Johnson & Johnson Institute. He serves on the clinical advisory board of Worrell, Inc. He has received research support from Olympus Corporation for projects outside of this paper. This manuscript is a review article and does not involve a research protocol requiring approval by the relevant institutional review board or ethics committee.

Electronic Supplementary Material

Supplementary material is available in the online version of this article at https://doi.org/10.1007/s11684-020-0784-7 and is accessible for authorized users.

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