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Scopus Q: Q1Subject: Simulation

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    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, Fatih
    Introduction 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.
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    Review
    Citation - WoS: 7
    Citation - Scopus: 7
    A Systematic Review on Classification and Assessment of Surgical Skill Levels for Simulation-Based Training Programs
    (Elsevier Ireland Ltd, 2023) Tonbul, Gokcen; Topalli, Damla; Cagiltay, Nergiz Ercil
    Background: Nowadays, advances in medical informatics have made minimally invasive surgery (MIS) procedures the preferred choice. However, there are several problems with the education programs in terms of surgical skill acquisition. For instance, defining and objectively measuring surgical skill levels is a challenging process. Accordingly, the aim of this study is to conduct a literature review for an investigation of the current approaches for classifying the surgical skill levels and for identifying the skill training tools and measurement methods.Materials and Methods: In this research, a search is conducted and a corpus is created. Exclusion and inclusion criteria are applied by limiting the number of articles based on surgical education, training approximations, hand movements, and endoscopic or laparoscopic operations. To satisfy these criteria, 57 articles are included in the corpus of this study.Results: Currently used surgical skill assessment approaches have been summarized. Results show that various classification approaches for the surgical skill level definitions are being used. Besides, many studies are con-ducted by omitting particularly important skill levels in between. Additionally, some inconsistencies are also identified across the skill level classification studies.Conclusion: In order to improve the benefits of simulation-based training programs, a standardized interdisci-plinary approach should be developed. For this reason, specific to each surgical procedure, the required skills should be identified. Additionally, appropriate measures for assessing these skills, which can be defined in simulation-based MIS training environments, should be refined. Finally, the skill levels gained during the developmental stages of these skills, with their threshold values referencing the identified measures, should be redefined in a standardized manner.