Current applications of artificial intelligence for intraoperative decision support in surgery
- PDF / 245,102 Bytes
- 13 Pages / 595.276 x 785.197 pts Page_size
- 29 Downloads / 170 Views
REVIEW
Current applications of artificial intelligence for intraoperative decision support in surgery Allison J. Navarrete-Welton1, Daniel A. Hashimoto (
✉)1,2
1 Surgical Artificial Intelligence and Innovation Laboratory, Massachusetts General Hospital, Boston, MA 02114, USA; 2Harvard Medical School, Boston, MA 02114, USA
© Higher Education Press 2020
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
Introduction In 1978, the cardiovascular surgeon Dr. Frank Spencer wrote that “a skillfully performed operation is about 75% decision making and 25% dexterity” [1]. While the exact split between technical skill and cognitive decisions can be debated and these domains often overlap, surgical practice requires complex decision making at each phase of care. The literature supports the importance of decision making (in both technical and non-technical aspects of care) in the outcome of a patient. In one recent study of surgical errors, cognitive errors were identified as a contributing factor to over half of the adverse events recorded [2]. Despite the relationship of the decision-making process to patient outcome, decision making skills are less emphasized than technical skills during surgical training, perhaps due to the difficulty of teaching decision making [3]. Furthermore, additional research suggests that decision-making skills vary with surgeon experience [4]. Thus, finding ways to
Received September 4, 2019; accepted March 14, 2020 Correspondence: Daniel A. Hashimoto, [email protected]
improve the quality of surgical decision making could help improve outcomes by optimizing surgical care. Intraoperative decision making has been well-studied — though predominantly through structured qualitative methods such as cognitive task analysis [4,5]. Flin et al. (2007) presented an excellent framework from which to cons
Data Loading...
