Anne van Dalen

A review on the current applications of artificial intelligence in the operating room I 227 8 DISCUSSION The results of this systematic review study provide an overview of various AI applications currently used for surgical purposes inside the OR. The great majority, of the AI applications have shown superior results in comparison to their non-AI alternatives. However, studies are set up in various pilot settings. The various applications are an indication of multi-field interest in finding use cases for AI in the OR, paired with a need for more clinical research across user settings. Many studies have shown significant technological performance in the field of AI, but only a small minority has been able to situate their impacts and associated changes in current health systems. 35 According to Rogers’ 36 widely used Diffusion of Innovations theory, adoption of innovative technology always involves early and late adopters. During the innovation process, where an individual is motivated to reduce uncertainty about the advantages and disadvantages of an innovation, it is important to emphasize the ethical and legal challenges. 37,38 Yet, sufficient political, regulatory, organizational, and clinical conditions for AI development and ethical use of sensitive information are still lacking and hence needed to implement AI applications safely and sustainably in the future. 35,39,40 Additional barriers for the widespread implementation of AI in health care may be unawareness on the topic or solutions, lack of user or implementation knowledge by the medical professionals and their workplace supporters, unresolved questions about ethics or privacy from management, or an insufficient IT infrastructure. Most likely, it will be a combination of these barriers. 41 While AI, and ML in particular, is receiving more attention in surgery, it is obviously not the only field of medicine in which the use of AI is growing. 27 The surgical field may be able to learn from the use of AI in other medical fields. For example, in oncology, research has demonstrated that ML applications can be of great help for the diagnosis or detection of cancer. 42-44 In cardiology, AI techniques are capable of reading electrocardiograms, and by integration with electronic medical records of patients, heart failure can be detected early onwith reducedmortality as outcome. 45–47 In anesthesiology, ANNs are used to monitor the depth of anesthesia, andML techniques are able to predict hypotension during surgery. 48,49 And now, during the current COVID-19 pandemic,