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. This randomized controlled trial compared real-time AI feedback to in-person expert instruction. Ninety-seven medical trainees completed a 90-min simulation training with five practice tumor resections followed by a realistic brain tumor resection. They were randomly assigned into 1-real-time AI feedback, 2-in-person expert instruction, and 3-no real-time feedback. Performance was assessed using a composite-score and Objective Structured Assessment of Technical Skills rating, rated by blinded experts. Training with real-time AI feedback (n = 33) resulted in significantly better performance outcomes compared to no real-time feedback (n = 32) and in-person instruction (n = 32), .266, [95% CI .107 .425], p < .001; .332, [95% CI .173 .491], p = .005, respectively. Learning from AI resulted in similar OSATS ratings (4.30 vs 4.11, p = 1) compared to in-person training with expert instruction. Intelligent systems may refine the way operating skills are taught, providing tailored, quantifiable feedback and actionable instructions in real-time.

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. DESIGN: A prospective 4-parallel-arm randomized controlled trial. SETTING: Neurosurgical Simulation and Artificial Intelligence Learning Centre, McGill University, Canada. PARTICIPANTS: Medical students (n = 120) from 4 Quebec medical schools. RESULTS: Participants completed a virtually simulated tumor resection task 5 times while receiving 1 of 4 feedback based on their group allocation: (1) practice-alone without feedback, (2) numerical feedback, (3) visual feedback, and (4) visuospatial feedback. Outcome measures were participants' scores on 14-performance metrics and the number of expert benchmarks achieved during each task. There were no significant differences in the first task which determined baseline performance. A statistically significant interaction between feedback allocation and task repetition was found on the number of benchmarks achieved, F (10.558, 408.257)=3.220, p < 0.001. Participants in all feedback groups significantly improved their performance compared to baseline. The visual feedback group achieved significantly higher number of benchmarks than the practice-alone group by the third repetition of the task, p = 0.005, 95%CI [0.42 3.25]. Visual feedback and visuospatial feedback improved performance significantly by the second repetition of the task, p = 0.016, 95%CI [0.19 2.71] and p = 0.003, 95%CI [0.4 2.57], respectively. CONCLUSION: Simulations with autonomous visual computer assistance may be effective pedagogical tools in teaching bimanual operative skills via visual and visuospatial feedback information delivery.