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Objectives: While the development of artificial intelligence (AI) and virtual reality (VR) technologies in medicine has been significant, their application to doctor-patient communication is limited. As communicating risk is a challenging, yet essential, component of shared decision-making (SDM) in surgery, this review aims to explore the current use of AI and VR in doctor-patient surgical risk communication., Methods: The search strategy was prepared by a medical librarian and run in 7 electronic databases. Articles were screened by a single reviewer. Included articles described the use of AI or VR applicable to surgical risk communication between patients, their families, and the surgical team., Results: From 4576 collected articles, 64 were included in this review. Identified applications included decision support tools (15, 23.4%), tailored patient information resources (13, 20.3%), treatment visualization tools (17, 26.6%) and communication training platforms (19, 29.7%). Overall, these technologies enhance risk communication and SDM, despite heterogeneity in evaluation methods. However, improvements in the usability and versatility of these interventions are needed., Conclusions: There is emerging literature regarding applications of AI and VR to facilitate doctor-patient surgical risk communication., Practice Implications: AI and VR hold the potential to personalize doctor-patient surgical risk communication to individual patients and healthcare contexts., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Background: Technology-enhanced teaching and learning, including Artificial Intelligence (AI) applications, has started to evolve in surgical education. Hence, the purpose of this scoping review is to explore the current and future roles of AI in surgical education., Methods: Nine bibliographic databases were searched from January 2010 to January 2021. Full-text articles were included if they focused on AI in surgical education., Results: Out of 14,008 unique sources of evidence, 93 were included. Out of 93, 84 were conducted in the simulation setting, and 89 targeted technical skills. Fifty-six studies focused on skills assessment/classification, and 36 used multiple AI techniques. Also, increasing sample size, having balanced data, and using AI to provide feedback were major future directions mentioned by authors., Conclusions: AI can help optimize the education of trainees and our results can help educators and researchers identify areas that need further investigation., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Importance: To better understand the emerging role of artificial intelligence (AI) in surgical training, efficacy of AI tutoring systems, such as the Virtual Operative Assistant (VOA), must be tested and compared with conventional approaches., Objective: To determine how VOA and remote expert instruction compare in learners' skill acquisition, affective, and cognitive outcomes during surgical simulation training., Design, Setting, and Participants: This instructor-blinded randomized clinical trial included medical students (undergraduate years 0-2) from 4 institutions in Canada during a single simulation training at McGill Neurosurgical Simulation and Artificial Intelligence Learning Centre, Montreal, Canada. Cross-sectional data were collected from January to April 2021. Analysis was conducted based on intention-to-treat. Data were analyzed from April to June 2021., Interventions: The interventions included 5 feedback sessions, 5 minutes each, during a single 75-minute training, including 5 practice sessions followed by 1 realistic virtual reality brain tumor resection. The 3 intervention arms included 2 treatment groups, AI audiovisual metric-based feedback (VOA group) and synchronous verbal scripted debriefing and instruction from a remote expert (instructor group), and a control group that received no feedback., Main Outcomes and Measures: The coprimary outcomes were change in procedural performance, quantified as Expertise Score by a validated assessment algorithm (Intelligent Continuous Expertise Monitoring System [ICEMS]; range, -1.00 to 1.00) for each practice resection, and learning and retention, measured from performance in realistic resections by ICEMS and blinded Objective Structured Assessment of Technical Skills (OSATS; range 1-7). Secondary outcomes included strength of emotions before, during, and after the intervention and cognitive load after intervention, measured in self-reports., Results: A total of 70 medical students (41 [59%] women and 29 [41%] men; mean [SD] age, 21.8 [2.3] years) from 4 institutions were randomized, including 23 students in the VOA group, 24 students in the instructor group, and 23 students in the control group. All participants were included in the final analysis. ICEMS assessed 350 practice resections, and ICEMS and OSATS evaluated 70 realistic resections. VOA significantly improved practice Expertise Scores by 0.66 (95% CI, 0.55 to 0.77) points compared with the instructor group and by 0.65 (95% CI, 0.54 to 0.77) points compared with the control group (P < .001). Realistic Expertise Scores were significantly higher for the VOA group compared with instructor (mean difference, 0.53 [95% CI, 0.40 to 0.67] points; P < .001) and control (mean difference. 0.49 [95% CI, 0.34 to 0.61] points; P < .001) groups. Mean global OSATS ratings were not statistically significant among the VOA (4.63 [95% CI, 4.06 to 5.20] points), instructor (4.40 [95% CI, 3.88-4.91] points), and control (3.86 [95% CI, 3.44 to 4.27] points) groups. However, on the OSATS subscores, VOA significantly enhanced the mean OSATS overall subscore compared with the control group (mean difference, 1.04 [95% CI, 0.13 to 1.96] points; P = .02), whereas expert instruction significantly improved OSATS subscores for instrument handling vs control (mean difference, 1.18 [95% CI, 0.22 to 2.14]; P = .01). No significant differences in cognitive load, positive activating, and negative emotions were found., Conclusions and Relevance: In this randomized clinical trial, VOA feedback demonstrated superior performance outcome and skill transfer, with equivalent OSATS ratings and cognitive and emotional responses compared with remote expert instruction, indicating advantages for its use in simulation training., Trial Registration: ClinicalTrials.gov Identifier: NCT04700384.

A growing body of work on intelligent tutoring systems, affective computing, and artificial intelligence in education is exploring creative, technology-driven approaches to enhance learners' experience of adaptive, positively-valenced emotions while interacting with advanced learning technologies. Despite this, there has been no published work to date that captures this topic's breadth. We took up this grand challenge by integrating related empirical studies and existing conceptual work and proposing a theoretically-guided taxonomy for the development and improvement of emotion-aware systems. In particular, multiple strategies system developers may use to help learners experience positive emotions are mapped out, including those that require different amounts and types of information about the user, as well as when this information is required. Examples from the literature are provided to illustrate how different emotion-aware system approaches can be combined to take advantage of different types of data, both prior to and during the learner-system interaction. High-level system features that emotion-aware systems can tailor to learners in order to elicit positive emotions are also described and exemplified. Theoretically, the taxonomy is primarily informed by the control-value theory of achievement emotions (Pekrun 2006, 2011) and its assumptions about the relationship between distal and proximal antecedents and the elicitation and regulation of emotion. The taxonomy expands upon a dichotomy of emotion-aware systems proposed by D'Mello and Graesser (2015) and is intended to guide the design of emotion-aware systems that can foster positive emotions during learner-system interactions through the use of varied approaches, data sources, and design features.

Artificial intelligence (AI) has gained momentum in the last decade in various professional domains, but its usage remains scarce in the field of medicine. Available AI-enhanced devices are not integrated in a consistent fashion throughout Canadian health facilities, and current medical practitioners and students are not well prepared for AI's impact on their careers. Undergraduate medical students lack fundamental knowledge of AI in medicine, from its impact on patient care and its potential as an adjunct decision-making tool, to the general fundamentals of how AI-enhanced devices work. Currently, postgraduates don't have access to AI-enhanced devices; this could potentially limit their understanding of how these devices might affect their future clinical practice. Canadian medical universities can play a critical role in familiarizing students with these newdevices. Incorporating newtopics into the already heavily charged medical curricula may be challenging, but students could make use of extracurricular activities to learn the concept of AI and strengthen interdisciplinary collaboration. Educational institutions would also need to propose policies for the safe and ethical use of devices in classrooms or internships. However, they might require guidance to draft new policies targeting AI in medical education. Canadian medical associations could take the lead to draft AI policies in healthcare to guide the equal and safe implementation of AI-enhanced devices across the Canadian medical community. Our paper will explore the work that has been done related to AI-specific policies in healthcare, focusing on Canada, and provide key points that could be used to organize future policies. [ABSTRACT FROM AUTHOR]