The Evolving Role of AI in Simulation-Based Medical Education: A Narrative Review.

Simulation Based Medical Education (SBME) has received a lot of attention in the past few years for providing medical students and practitioners near real-life opportunities to practice and improve their clinical and non-clinical skills (Issenberg & Scales, 2008) in a relatively risk-free environment, learn from their errors and encounter rare clinical events that they might not experience in an actual clinical environment. SBME has been introduced in medical context as a result of changes in providing health care services, reducing working hours for medical staff, ethical issues in using patients for educational purposes, and reducing unnecessary risks. These changes have resulted in limited patient contact for educational purposes. It has been predicted that SBME "in all its forms will be a vital part of building a safer healthcare system" (Department of Health, 2008, p.55) in near future. A networked learning (NL) approach to blended SBME has been introduced in an innovative NHS simulation centre (SC) in North West England in 2011. This new model replaces existing face-to-face briefing sessions with on-line resources. As per existing practices, following introductory briefings, students and facilitators will continue to engage in simulated clinical procedures and then collaboratively debrief their experiences. SBME attempts to create an authentic context with student-to-student, student-facilitator, and even student-simulated patient social interactions. Student-simulated patient social interactions are mediated by clinical staff 'speaking through' high fidelity mannequins, providing feedback on students' skills, communications and emotions. With the introduction of blended learning students will also have the option to participate in continued reflection in a NL environment. The focus of this on-going case study is to investigate the teaching and learning experiences, perceptions, and challenges that learners and facilitators may confront in the NL environment and provides the opportunity to focus "on the connections between learners, learners and tutors, and between learners and the resources they make use of in their learning" (Jones, et. al, p. 90). Blended SBME has been designed to provide students with flexible and collaborative learning opportunities to prepare themselves for the simulated scenarios, but results from a pilot study suggest this transition may prove challenging in terms of disrupting existing Trust teaching and learning cultures. As situated learning (Lave and Wenger, 1991) and experiential learning (Kolb, 1984) theories underpin SBME literature, both will be examined and critiqued in the NL context over the two-year course of this study.

Comparision of psychological fidelity of drama and simulation based medical education (DSBME) and simulation based medical education (SBME) – A study design

Simulation-based medical education (SBME) is a critical training tool in healthcare, shaping learners' skills, professional identities, and inclusivity. Leadership demographics in SBME, including age, gender, race/ethnicity, and medical specialties, influence program design and learner outcomes. Artificial intelligence (AI) platforms increasingly generate demographic data, but their biases may perpetuate inequities in representation. This study evaluated the demographic profiles of simulation instructors and heads of simulation labs generated by three AI platforms-ChatGPT, Gemini, and Claude-across nine global locations. A global cross-sectional study was conducted over 5days (November 2024). Standardized English prompts were used to generate demographic profiles of simulation instructors and heads of simulation labs from ChatGPT, Gemini, and Claude. Outputs included age, gender, race/ethnicity, and medical specialty data for 2014 instructors and 1880 lab heads. Statistical analyses included ANOVA for continuous variables and chi-square tests for categorical data, with Bonferroni corrections for multiple comparisons: P significant < 0.05. Significant demographic differences were observed among AI platforms. Claude profiles depicted older heads of simulation labs (mean: 57years) compared to instructors (mean: 41years), while ChatGPT and Gemini showed smaller age gaps. Gender representation varied, with ChatGPT and Gemini generating balanced profiles, while Claude showed a male predominance (63.5%) among lab heads. ChatGPT and Gemini outputs reflected greater racial diversity, with up to 24.4% Black and 20.6% Hispanic/Latin representation, while Claude predominantly featured White profiles (47.8%). Specialty preferences also differed, with Claude favoring anesthesiology and surgery, whereas ChatGPT and Gemini offered broader interdisciplinary representation. AI-generated demographic profiles of SBME leadership reveal biases that may reinforce inequities in healthcare education. ChatGPT and Gemini demonstrated broader diversity in age, gender, and race, while Claude skewed towards older, White, and male profiles, particularly for leadership roles. Addressing these biases through ethical AI development, enhanced AI literacy, and promoting diverse leadership in SBME are essential to fostering equitable and inclusive training environments. Not applicable. This study exclusively used AI-generated synthetic data.

Simulation-based medical education (SBME) has been widely implemented in skill training in various clinical specialties. SBME has contributed not only to patient and medical safety but also to undergraduate and specialist education in the healthcare field. In this review, we discuss the challenges and future directions of SBME in the artificial intelligence (AI) era. While SBME fidelity or methods may become highly complicated in the AI era, the fact is that learners play a central role. As SBME and clinical education are complementary, mutual feedback and improvement are essential, especially in non-technical skill development. For the development of sustainable SBME in the clinical field in the AI era, continuous improvement is needed by academia, educators, and learners.

Simulation-Based Mastery Learning With Virtual Coaching: Experience in Training Standardized Upper Endoscopy to Novice Endoscopists

A guide to curriculum inquiry for brachytherapy simulation-based medical education

Simulation is a synthetic representation of a real-world process with sufficient reliability to facilitate learning through contemplation and practice without the hazard, innate in a real-life experience. Nowadays, simulation is a useful accompaniment to medical education as pre-exposure to necessary clinical skills as exposure in the real clinical setting may be insufficient. Clinical skills and performance are considered core proficiency and are crucial to the professionals. This can enable the students to familiarize themselves with patient examination and hands-on- training by using models before coming across patients directly. Simulators are broadly classified into two broad categories: 1. High-fidelity Simulators and 2. Low-fidelity simulators. The fidelity of a simulator is decided by the extent to which it provides realism through characteristics. Simulation Based Medical Education (SBME) provides a safe environment for the students to acquire their psychomotor skill but is not necessarily better than other types of instruction as there is a high degree of variability between studies. SBME has been introduced in the health care field and now it is becoming one of the most popular teaching techniques for improving patient safety and care. It would be advantageous if it is included in medical curricula as it may proof boon for the young medicos. Keywords: Medical simulation; SBME; Simulators

Background Artificial intelligence (AI) is increasingly reshaping medical education through personalized learning, adaptive assessments, and advanced simulations. This systematic narrative review synthesizes the theoretical development of AI in medical training, focusing on educational models, frameworks, learning outcomes, and stakeholder considerations. A literature search of PubMed, Scopus, Web of Science, and Google Scholar (January 2000–March 2025) identified 1,288 records, of which 48 studies met the inclusion criteria and were included in qualitative thematic synthesis. No statistical meta-analysis was conducted due to methodological heterogeneity. Results Five major AI domains emerged: Intelligent Tutoring Systems, Simulation-Based Medical Education, Adaptive Learning, Generative AI, and Explainable AI. These domains align with established instructional theories and contribute to improved engagement and learning efficiency. However, concerns persist regarding learner deskilling, academic integrity, and algorithmic bias. AI integration influences multiple stakeholders, including trainees, educators, clinicians, policymakers, and patients. The field has progressed from rule-based approaches to data-driven machine learning models, enabling personalized instruction. Responsible implementation necessitates addressing pedagogical, ethical, and practical challenges, while also reducing the global digital divide. Conclusions This systematic review provides guidance for educators, researchers, and policymakers on integrating AI effectively and ethically into medical education.

Deliberate Practice in Simulation-Based Surgical Skills Training: A Scoping Review

Simulation-based medical education: Cost measurement must be comprehensive

Simulation-based medical education (SBME) is increasingly used in emergency medicine (EM) training to enhance clinical skills and decision-making. However, its impact on undergraduate clerkship performance and student perceptions in the Middle Eastern context remains underexplored. This study aimed to evaluate whether the integration of high-fidelity simulation into a medical student EM clerkship in Qatar improves academic outcomes and enhances student satisfaction with the learning experience. Two clerkship students cohorts were compared: 63 students in a lecture-based education (LBE) group in 2022 and 67 students in an SBME group in 2024. Multiple-choice question (MCQ) and objective structured clinical examination (OSCE) scores were analyzed using independent sample t-tests. Demographic variables (age, gender) were collected, and qualitative feedback from the SBME group was analyzed using descriptive content analysis. There were no statistically significant differences in academic performance between the lecture-based education (LBE) and simulation-based medical education (SBME) cohorts. The mean MCQ score was 29.2 (SD = 4.1) for the LBE group and 28.8 (SD = 4.3) for the SBME group (p = 0.588), with no meaningful difference (mean difference = +0.4, 95% CI: [-1.08, 1.84], Cohen’s d = +0.10). OSCE scores were also comparable, with the LBE group scoring a mean of 24.8 (SD = 1.8) and the SBME group 25.2 (SD = 1.7) (p = 0.192; mean difference = -0.4, 95% CI: [-1.01, +0.21], Cohen’s d = -0.23). Demographic characteristics were also similar between groups, with a mean age of approximately 23 years and around 70% of participants being female. Thematic analysis of feedback revealed three dominant themes: (1) Enhanced clinical preparedness and confidence – students felt better prepared for real emergencies after simulation practice; (2) Active learning and realism – the lifelike scenarios and hands-on approach helped bridge theory to practice in a safe environment; (3) Positive engagement and recommendations – students found simulation highly engaging and recommended increasing its use. One student wrote, “The simulations were the most valuable part of the rotation, boosting my confidence in handling acute cases.” Minor challenges noted included initial anxiety during simulations and scheduling constraints, but overall perceptions were overwhelmingly positive. While SBME in the emergency medicine clerkship did not lead to statistically significant improvements in exam performance, it was highly valued by students and associated with enhanced confidence, clinical preparedness, and engagement. These results support the continued integration of SBME into EM clerkship as a complementary approach to traditional teaching. Future studies with larger sample sizes and extended follow-up are warranted to evaluate the long-term impact and broader applicability of simulation-based medical education.

Regional anaesthesia has experienced a global resurgence over recent decades alongside increasing demand for anaesthetists to be competent in its delivery. This was reflected in a recent UK anaesthetic curriculum update, which mandates a wide range of specific regional anaesthesia competencies. Despite this, regional anaesthesia education remains ad hoc and inconsistent. Innovative technologies (artificial intelligence, virtual reality, and augmented reality) are increasingly integrated within medical education, and have the potential to transform training practices. This scoping review aimed to explore the ways in which innovative technologies are currently used in regional anaesthesia, and to consider their role in education and training. This review was conducted using established frameworks for scoping reviews, and included searches of three major databases, alongside targeted citation searching. Data were analysed numerically, followed by reflexive thematic analysis. In total, 855 citations were identified. After removal of duplicates and abstract eligibility screening, 106 full-text articles were assessed and 38 met the criteria for inclusion. The majority of studies were published in the last 2 yr and a lack of high-quality evidence, particularly focussing on educational outcomes, was noted. A wide range of applications for innovative technologies in regional anaesthesia education were described including roles in anatomy education, accelerated skill acquisition, simulation-based medical education, and assessment. Innovative technologies were associated with benefits such as provision of reliable learning experiences, reduced supervisory requirements, and enhanced educational outcomes. Future educators should consider their utility and provide structured evaluation. Significant heterogeneity was noted in the literature base and further research is recommended, specifically studying primary educational outcomes.

Objective: Simulation based medical education (SBME) is fast becoming embedded into undergraduate medical curricula with many publications now describing its various modes and student self-reported impacts. This systematic review synthesizes the available literature for evidence of performance effects of SBME as an adjunct within traditional teaching programmes. Methods: A narrative systematic review was conducted according to PRISMA guidelines using Ovid MEDLINE, EMBASE, and PubMed databases for studies, published in English, reporting on general medical and surgical undergraduate SBME between 2010 to 2020. Two reviewers independently assessed potential studies for inclusion. Methods and topics of simulation with their assessments were evaluated. Descriptive statistics were used to describe pooled student cohorts. Results: 3074 articles were initially identified using the search criteria with 92 full-text articles then screened for eligibility. Nineteen articles, including nine randomised trials, concerning 2459 students (median 79/study), were selected for review. Cardiac scenarios were commonest (n=6) with three studies including surgical topics. Nine studies used mannequin simulators (median time/session 17.5minutes) versus standardised patients in seven (median time/session=82 minutes). Educational impact was measured by written (n=10), checklist (n=5) and OSCEs (n=3) assessment either alone or in combination (n=1, OSCE/written assessment). All articles reported a positive effect of SBME on knowledge including improved retention in three.Conclusion: SBME, as an adjunct to existing curricula, improves knowledge-based performance of medical students at least in the short-term. Future studies should broaden its topics, assess longer term impacts and cost-effectiveness while also considering whether and what areas of traditional undergraduate learning it can replace.

Assessing perspectives on artificial intelligence applications to gastroenterology

It is an critical issue to promote effective learning in simulation-based medical ed-ucation（SBME）. Feedback debriefing, identified as the cornerstone of simulation experience, plays an important role in learning facilitation. This article served as an introduction to feedback debriefing and covered a range of topics including a brief review of its origin, relevant theoretical foundation, structure and process of debriefing in the context of SBME and evaluation of its utility in clinical practice. According to the current status of domestic and overseas research, potent debriefing should be emphasized to improve the effectiveness of SBME and future performance of individual and group. Key words: Debriefing; Feedback; Simulation-based medical education; Experiential learning