Effect of Artificial Intelligence Tutoring vs Expert Instruction on Learning Simulated Surgical Skills Among Medical Students
Pourquoi ce travail est dans la base
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle