Real-Time multifaceted artificial intelligence vs In-Person instruction in teaching surgical technical skills: a randomized controlled trial.

Trainees develop surgical technical skills by learning from experts who provide context for successful task completion, identify potential risks, and guide correct instrument handling. This expert-guided training faces significant limitations in objectively assessing skills in real-time and tracking learning. It is unknown whether AI systems can effectively replicate nuanced real-time feedback, risk identification, and guidance in mastering surgical technical skills that expert instructors offer.

Effect of Feedback Modality on Simulated Surgical Skills Learning Using Automated Educational Systems- A Four-Arm Randomized Control Trial.

Objective: To explore optimal feedback methodologies to enhance trainee skill acquisition in simulated surgical bimanual skills learning during brain tumor resections.

Hypotheses: (1) Providing feedback results in better learning outcomes in teaching surgical technical skill when compared to practice alone with no tailored performance feedback. (2) Providing more visual and visuospatial feedback results in better learning outcomes when compared to providing numerical feedback.

AI in Surgical Curriculum Design and Unintended Outcomes for Technical Competencies in Simulation Training.

Importance: To better elucidate the role of artificial intelligence (AI) in surgical skills training requires investigations in the potential existence of a hidden curriculum.

Objective: To assess the pedagogical value of AI-selected technical competencies and their extended effects in surgical simulation training.

Design, Setting, And Participants: This cohort study was a follow-up of a randomized clinical trial conducted at the Neurosurgical Simulation and Artificial Intelligence Learning Centre at the Montreal Neurological Institute, McGill University, Montreal, Canada.

Performance in a Simulated Virtual Reality Anterior Cervical Discectomy and Fusion Task: Disc Residual, Rate of Removal, and Efficiency Analyses.

Background And Objectives: Anterior cervical discectomy and fusion (ACDF) is among the most common spine procedures. The Sim-Ortho virtual reality simulator platform contains a validated ACDF simulated task for performance assessment. This study aims to develop a methodology to extract three-dimensional data and reconstruct and quantitate specific simulated disc tissues to generate novel metrics to analyze performance metrics of skilled and less skilled participants.

Nondominant Hand Skills Spatial and Psychomotor Analysis During a Complex Virtual Reality Neurosurgical Task-A Case Series Study.

Background: Virtual reality surgical simulators provide detailed psychomotor performance data, allowing qualitative and quantitative assessment of hand function. The nondominant hand plays an essential role in neurosurgery in exposing the operative area, assisting the dominant hand to optimize task execution, and hemostasis. Outlining expert-level nondominant hand skills may be critical to understand surgical expertise and aid learner training.

Continuous monitoring of surgical bimanual expertise using deep neural networks in virtual reality simulation.

In procedural-based medicine, the technical ability can be a critical determinant of patient outcomes. Psychomotor performance occurs in real-time, hence a continuous assessment is necessary to provide action-oriented feedback and error avoidance guidance. We outline a deep learning application, the Intelligent Continuous Expertise Monitoring System (ICEMS), to assess surgical bimanual performance at 0.

Effect of Artificial Intelligence Tutoring vs Expert Instruction on Learning Simulated Surgical Skills Among Medical Students: A Randomized Clinical Trial.

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.

Assessment of learning curves on a simulated neurosurgical task using metrics selected by artificial intelligence.

Objective: Understanding the variation of learning curves of experts and trainees for a given surgical procedure is important in implementing formative learning paradigms to accelerate mastery. The study objectives were to use artificial intelligence (AI)-derived metrics to determine the learning curves of participants in 4 groups with different expertise levels who performed a series of identical virtual reality (VR) subpial resection tasks and to identify learning curve differences among the 4 groups.

Methods: A total of 50 individuals participated, 14 neurosurgeons, 4 neurosurgical fellows and 10 senior residents (seniors), 10 junior residents (juniors), and 12 medical students.