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Article Artificial Intelligence Based Resuscitation Simulation: A Pilot Study of a Novel Approach to Team Leadership Training(BMC, 2026) Kanbakan, Altug; Berikol, Goksu Bozdereli; Ilhan, Bugra; Altintas, Emel; Doganay, FatihIntroduction Team leadership training is essential alongside with technical training for effective resuscitation management. Addressing this gap, we developed a novel simulation system leveraging Large Language Models (LLMs) to create Artificial Intelligence (Al) agents simulating team members in Advanced Cardiovascular Life Support (ACLS) scenarios. This pilot study aimed to to develop a novel LLM-based ACLS simulation training platform and evaluate its performance in simulated resuscitation scenarios on established protocols.
Method Using the Claude 3.5 Sonnet API, we designed a simulation system with four Al agents assigned specific roles as healthcare staff within an ACLS team. Each agent strictly followed the 2020 American Heart Association (AHA) ACLS guidelines while interacting with an ACLS certified emergency medicine specialist user. The ten patient scenario transcripts were evaluated with three blinded emergency medicine specialists whether all the recommended steps are completed. Inter-rater reliability was assessed using Kendall's W and Krippendorff's Alpha statistics to evaluate agreement both within raters and the model.
Results Al agents consistently adhered to the AHA 2020 ACIS algorithm across scenarios, with a high inter-rater reliability (Kendall's W > 0.75 ) . Krippendorff's Alpha values for agreement ranged from substantial (0.84) to almost perfect (0.99), indicating robust compliance with guidelines and effective simulation of resuscitation responses.
Conclusion This study highlights the potential of LL.M-powered simulations as an adjunct to traditional resuscitation training. The system effectively supported team leadership training by providing consistent and guideline-compliant responses. While the results are promising, further research with larger participant samples is necessary to evaluate the long-term educational impact and scalability of such systems.Review Citation - WoS: 31Citation - Scopus: 37The Effectiveness of Simulation-Based Team Training in Obstetrics Emergencies for Improving Technical Skills a Systematic Review(Lippincott Williams & Wilkins, 2020) Yucel, Cigdem; Hawley, Glenda; Terzioglu, Fusun; Bogossian, FionaThis review explores the effectiveness of simulation-based team training in obstetric emergencies for improving technical skills. A literature search was conducted that included all articles to January 2018. A total of 21 articles were included from a potential 1327 articles. Each included study was assessed for impact of the training program using Kirkpatrick's 4-level model. Only the performance of technical skills was evaluated. Five studies reported on acceptance of simulation as an education tool at a level 1. Level 2 outcomes were reported in 7 studies where staff demonstrated improved skills in an educational setting. Three studies reported improved performance in a clinical setting at a level 3. Ten studies were categorized as level 4 and found that simulation learning was translated into improved techniques or maneuvers in reduced time frames in emergency situations of shoulder dystocia and postpartum hemorrhage. There was evidence that neonatal outcomes were improved.
