Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 9 appraisals
Medicine
Exploring the role of generative artificial intelligence in enhancing clinical skills training: A bibliometric analysis
BACKGROUND: Traditional clinical skills training faces challenges such as limited standardized patient resources, high costs of simulation equipment, and restricted opportunities for repeated practice. Generative artificial intelligence (GAI), particularly large language models, has attracted increasing attention as a potential tool for simulation, feedback, and adaptive learning in medical education. However, the research landscape and thematic development of GAI in clinical skills training remain insufficiently mapped. METHODS: A bibliometric analysis was conducted using literature retrieved from the Web of Science Core Collection from January 1, 2011 to April 1, 2025. VOSviewer, the Bibliometrix R package, and CiteSpace were used to analyze publication trends, country and institutional contributions, journal distribution, collaboration networks, keyword co-occurrence, co-cited references, and citation bursts. RESULTS: A total of 322 publications were included. Research activity remained limited before 2023 but increased rapidly thereafter. The United States contributed the largest number of publications, followed by China and India. Major contributing institutions included the National University of Singapore, Gazi University, and Nova Southeastern University. Frequently publishing journals included JMIR Medical Education, Medical Teacher, and BMC Medical Education. Keyword and thematic analyses showed that current research attention is mainly concentrated on ChatGPT, large language models, natural language processing, clinical reasoning simulation, personalized learning, virtual patient interaction, and ethical governance. CONCLUSIONS: Research on GAI in clinical skills training is in an early but rapidly expanding stage, with growing scholarly attention to language-model-driven educational applications and related ethical issues. The bibliometric findings reflect research activity, knowledge structure, and thematic priorities rather than direct evidence of educational effectiveness. Future studies should adopt rigorous empirical designs and standardized evaluation frameworks to assess the effectiveness, safety, and appropriate boundaries of GAI applications in clinical skills training.
19 July 2026
Read appraisal →JMIR nursing
Simulation-Based Training for Nursing Students to Improve Patient Safety: Systematic Review
BACKGROUND: Patient safety is a fundamental pillar of health care quality. Simulation-based training provides a controlled environment for nursing students to develop safety competencies and error-recognition skills before clinical practice. OBJECTIVE: This systematic review aimed to describe and characterize the simulation-based education features and modalities used to address patient safety outcomes in undergraduate nursing students, identifying the strategies that contribute to improvements in safety-related competencies. METHODS: A systematic review was conducted following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines across PubMed, Web of Science, Scopus, CINAHL, Cochrane, and Lilacs (2019-2024). Inclusion criteria focused on original studies involving undergraduate nursing students and simulation interventions measuring patient safety outcomes. Studies in languages other than English, Spanish, or Portuguese were excluded. Two reviewers independently performed study selection and data extraction. Methodological quality was assessed using Joanna Briggs Institute tools, applying a 60% quality threshold for inclusion. Results were synthesized through a narrative approach. RESULTS: A total of 20 studies from 12 countries were included. The methodological quality was high (n=14) and moderate (n=6). Findings revealed that high-fidelity simulation and virtual reality are the primary strategies used. Simulation proved effective in enhancing both technical skills (medication administration accuracy) and nontechnical skills (communication via SBAR [Situation, Background, Assessment, Recommendation] and ISBAR [Identification, Situation, Background, Assessment, Recommendation] tools, teamwork, and adverse event reporting). Key strategies contributing to safety included repetitive practice and interprofessional simulation, which significantly improved error detection and clinical judgment. CONCLUSIONS: Simulation is an essential pedagogical strategy for preparing nursing students to deliver safe care. Practical implications include the need to integrate structured simulation into nursing curricula to bridge the theory-practice gap. Future research should prioritize longitudinal designs to assess the retention of these safety skills in clinical settings and develop standardized metrics for measuring patient safety outcomes.
28 May 2026
Read appraisal →Medical education online
AI-assisted case-based learning and flipped classroom to improve clinical decision-making: a randomized controlled trial in reproductive medicine
BACKGROUND: Efficient training of reproductive medicine clinicians is critical in the context of declining global fertility and increasing infertility. Traditional lecture‑based instruction often fails to sufficiently develop clinical decision‑making skills within limited residency rotations. Innovative strategies that integrate artificial intelligence (AI) with case‑based learning (CBL) and flipped classroom (FC) formats may enhance clinical reasoning, however rigorous evidence in reproductive medicine education remains limited. METHODS: We conducted a randomized controlled trial involving 50 obstetrics and gynecology residents at the First Hospital of Jilin University. Participants were randomly assigned to an AI‑assisted CBL+FC group or a traditional lecture control group. The AI‑assisted CBL+FC group completed pre‑class interactive case work with virtual standardized patients on the DoctorU platform and case analyses on the Superstar Learning platform, followed by interactive in‑class discussions. Primary outcomes included post‑course theoretical knowledge tests, Mini‑Clinical Evaluation Exercise (Mini‑CEX), and Objective Structured Clinical Examination (OSCE) scores. Secondary outcomes assessed learner motivation, clinical thinking, self‑directed learning, and perceived course effectiveness using a 5‑point Likert scale. RESULTS: Baseline characteristics were comparable between groups. After the intervention, the AI‑assisted CBL+FC group achieved significantly higher theoretical test scores than the control group. The AI‑assisted CBL+FC group also demonstrated superior overall clinical competence in Mini‑CEX assessments and higher OSCE total scores. Participants in the AI‑assisted CBL+FC group reported greater improvements in learning motivation, clinical reasoning, self‑directed learning, and perceived course effectiveness. CONCLUSIONS: The AI‑assisted CBL+FC instructional model significantly enhances theoretical knowledge, clinical decision‑making skills, and learner engagement among reproductive medicine residents. This blended learning model offers an efficacious and generalizable methodology for training practitioners to address the evolving clinical requirements within contemporary fertility care.
28 May 2026
Read appraisal →JMIR formative research
Feasibility of Large Language Model-Based Standardized Virtual Patients to Support Clinical Decision-Making Training in Operative Dentistry: Mixed Methods Study
BACKGROUND: Clinical decision-making training in operative dentistry commonly relies on real or standardized patients to develop undergraduate students' ability to deliver safe, effective, and patient-centered care. However, training with real or standardized patients can be limited in scalability, cost-effectiveness, and accessibility. Large language models, with their human-like language capabilities, may have the potential to simulate patients in clinical encounters and help overcome some limitations associated with traditional training approaches. OBJECTIVE: This study aimed to evaluate the feasibility of using large language model-based standardized virtual patients to support undergraduate dental students' clinical decision-making training in operative dentistry. METHODS: This mixed methods cross-sectional feasibility study was conducted during a simulation-based clinical decision-making training session in the Operative Dentistry and Cariology course at the College of Dentistry, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia. Eligible participants were second-year undergraduate dental students enrolled in the course. A convenience sampling approach was used, with all eligible students (N=50) invited to participate. A total of 41 students completed the study, 23 (56%) of whom were male. The students were divided into 8 groups. Each group interacted with 2 standardized virtual patients powered by ChatGPT-4o (OpenAI) through the Chatbase platform to complete comprehensive history-taking and then reviewed the standardized virtual patients' intraoral photographs and bitewing radiographs. For each standardized virtual patient, students as a group recorded diagnoses, performed a risk assessment, and formulated a treatment plan. Students then completed the Student Satisfaction and Self-Confidence in Learning questionnaire. The quality of the standardized virtual patient responses and overall dialogue realism were evaluated using the Dialogue Authenticity Scale. The dialogues were also thematically analyzed to identify authenticity-undermining response features and explore their context and underlying causes. RESULTS: Students perceived the simulation-based training session positively, with all questionnaire items showing high median scores (4.00-5.00 on a 5-point scale), and both item-level IQRs and 95% CIs spanning no more than 1.0 scale point. In addition, standardized virtual patient responses were largely authentic, with an overall median authenticity rating of 4.50 (IQR 4.00-5.00; 95% CI 4.00-5.00) on a 6-point scale across all interactions. However, several authenticity-undermining response features were identified, including responses that were inconsistent with typical human behavior, contained information beyond a patient's likely knowledge, or were factually incorrect. CONCLUSIONS: This proof-of-concept study supports the feasibility of implementing large language model-based standardized virtual patients in undergraduate simulation-based clinical decision-making training in operative dentistry. In a dental context where this application has been only minimally evaluated, this study provides early evidence of positive student perceptions, acceptability, and largely authentic dialogue, while also identifying important performance limitations. Further research is warranted to optimize performance and to evaluate the educational effectiveness of this approach in improving undergraduate students' clinical skills and knowledge.
20 May 2026
Read appraisal →Journal of medical Internet research
GenAI-Supported Virtual Patients in Health Care Education: Systematic Review
BACKGROUND: Generative artificial intelligence (GenAI) is enhancing virtual patient simulations in health care education by enabling dynamic, adaptive interactions, reshaping how clinical skills are taught. A synthesis of the current evidence is needed to guide implementation and future research, given the pace of technological advancement. OBJECTIVE: This systematic review aims to synthesize empirical research on the design, implementation, and educational impact of GenAI-supported virtual patients in health care education. METHODS: A systematic search was conducted across 5 databases (CINAHL, Medline, Embase, Scopus, and Web of Science) from their inception to March 19, 2026. Reference lists of included studies and relevant systematic reviews were also screened. Peer-reviewed studies in English that evaluated GenAI-supported virtual patients using quantitative or mixed methods were included. Two reviewers independently screened studies and extracted data. Study quality and risk of bias were assessed critically using JBI (Joanna Briggs Institute) checklists, with disagreements resolved by consensus. RESULTS: A total of 15 studies met the inclusion criteria (total participants N=645), spanning health care disciplines, including nursing, medicine, pharmacy, radiography, and medical first-responder training. The virtual patients varied in design; input modalities included text (9 studies), voice (5 studies), or hybrid (1 study); output was text (9 studies), speech (5 studies), or both (1 study); 6 studies used 3D-embodied avatars, while 9 used nonembodied interfaces. A total of 13 studies used OpenAI GPT models (eg, ChatGPT), 1 used a fine-tuned model from a different provider, and 1 evaluated multiple model families (Claude, GPT, and open-source). Further, 6 studies used controlled experimental designs, including 3 randomized controlled trials (RCTs); the remainder were cross-sectional or prepost evaluations. Primary outcomes included user perceptions (14 studies), communication skills (4 studies), clinical reasoning (3 studies), and performance (7 studies). In controlled comparisons, GenAI-supported virtual patients consistently improved outcomes relative to control conditions: for example, enhanced clinical decision-making (RCT, n=21), ophthalmology history-taking skills (RCT, n=26), and medical history-taking performance (crossover RCT, n=20). The evidence base is characterized by brief intervention durations, a predominant reliance on single-session interactions, and a general lack of underpinning educational theory. No meta-analysis was performed due to the limited number of studies and significant heterogeneity in designs, interventions, and outcome measures. CONCLUSIONS: The evidence supports the feasibility and acceptability of GenAI-supported virtual patients, with positive learner perceptions and promising outcomes for skills development. However, critical limitations remain in emotional-behavioral complexity, simulation adaptability, and research design rigor (eg, limited use of control groups and validated instruments). The review offers educators, instructional designers, and policymakers actionable insights for integrating dynamic, artificial intelligence-driven simulations while identifying crucial gaps-such as the need for theoretical grounding, longitudinal studies, and standardized design protocols-that must be addressed for safe and effective implementation.
11 May 2026
Read appraisal →African journal of reproductive health
Effects of Simulation-Based Mastery Learning versus Deliberate Practice on midwifery students' knowledge, skills and self confidence in managing the third stage of labour: A quasi-experimental study
Achieving maternal health care coverage by 2030 requires strengthening the midwifery workforce. To ensure accessible, culturally appropriate, and high-quality care, midwives must receive education aligned with global standards. This study aimed to evaluate the effect of a 20 hours Simulation-Based Mastery Learning (SBML) combined with Deliberate Practice (DP) and the Objective Structured Clinical Examination (OSCE) on midwifery students' knowledge, skills, and self-confidence in managing the third stage of labor. Sixty midwifery students were randomly allocated to experimental (n = 30) and control (n = 30) groups. Following the intervention, all students met or surpassed the minimum passing standard (MPS), achieving at least 90% in skills (Z=-4.791; p<.001; r~0.87), 80% in knowledge (Z=-4.814; p<.001, r~0.87 ), and an increased score in self-confidence (Z=-4.792; p<.001; r~0.87 ). These improvements were maintained after the clinical experience. The performance of students trained through the traditional approach was significantly lower than that of those trained with SBML, and none of them achieved the MPS. This study highlights the need to structure nursing and midwifery training programs according to the Theory-SBML-Clinical placement model to strengthen students' competencies. La réalisation de la couverture universelle des soins de santé maternelle d’ici 2030 passe par un renforcement substantiel de la profession de sage-femme. Pour assurer des soins accessibles, culturellement adaptés et de qualité optimale, il est essentiel que les sages-femmes bénéficient d’une formation conforme aux normes internationales. Cette étude avait pour objectif d’évaluer l’impact de 20 heures d’apprentissage par simulation fondé sur la pédagogie de la maîtrise, la pratique délibérée, et l’Examen Clinique Objectif Structuré (ECOS) sur les connaissances, les compétences et la confiance en soi des étudiantes sages-femmes dans la prise en charge de la troisième phase de l’accouchement. Un total de 60 étudiantes sages-femmes ont été réparties de façon aléatoire en un groupe expérimental (n = 30) et un groupe témoin (n = 30). À la suite de l’intervention, toutes les étudiantes ont atteint ou dépassé la norme minimale de réussite (MPS), obtenant au moins 90 % en compétences (Z=-4.791; p<.001 ; r~0.87), 80% en connaissances (Z=-4.814; p<.001 ; r~0.87), ainsi qu’une amélioration de leur score de confiance en soi (Z=-4.792; p<.001 ; r~0.87). Ces progrès ont été conservés après l’expérience clinique. Les performances des étudiantes formées selon l’approche traditionnelle étaient significativement inférieures, et aucune d’entre elles n’a atteint le Seuil Minimal de Réussite (SMR). L’apprentissage par maîtrise, la pratique délibérée et l’ECOS apparaissent comme des éléments essentiels au développement d’un programme de formation par simulation de haute qualité, favorisant un transfert efficace des acquis vers la pratique clinique.
15 Apr 2026
Read appraisal →BMJ open
Challenging history taking encounters: a systematic review, meta-analysis and phenomenological framework
OBJECTIVES: All physicians will experience challenging history taking encounters, where communication is impaired and negatively impacts the diagnostic process. The aims of this systematic review were to (1) undertake a meta-analysis of the frequency of challenging encounters; (2) collate adverse outcomes of challenging encounters; (3) identify underlying causes of challenging encounters; (4) identify strategies to deal with different challenges; and (5) align these strategies with our published phenomenological framework of history taking challenges. DESIGN: This was a systematic review and meta-analysis of prevalence data adhering to the Preferred Reporting Items for Systematic reviews and Meta-Analyses and the Meta-analyses of Observational Studies in Epidemiology guidelines. DATA SOURCES: A literature search in MEDLINE, Embase and Cochrane databases was performed on 12 July 2020, and updated on 4 August 2025, focusing on challenging history taking encounters in any clinical setting. ELIGIBILITY CRITERIA: Articles reporting on the frequency, adverse outcomes, causative factors or strategies used to address challenges in the history taking process in any clinical area of medicine. DATA EXTRACTION AND SYNTHESIS: Factors associated with challenging history encounters (causative or consequential) were categorised using inductive coding and referenced to a phenomenological framework. Meta-analysis was used to estimate the prevalence of history taking encounters using a restricted maximum likelihood model with τ 2 and I 2 as tests for heterogeneity and funnel plot with Egger's test for publication bias. RESULTS: 73 articles were included in the analysis. The overall prevalence of challenging history taking encounters was 19.5% (95% CI 14.2% to 24.7%). Adverse outcomes of patient dissatisfaction (level 1 evidence) and diagnostic uncertainty (level 3 evidence) were identified. Factors associated with (n=22) and strategies to mitigate challenging encounters (n=13) were categorised. Correlation of factors and strategies with a phenomenological approach created a framework to assist novice history takers in approaching such circumstances. CONCLUSIONS: Challenging history taking encounters are common. Little is known of the relative importance of factors associated with challenging history taking encounters or the impact of suggested strategies. Many of the suggested strategies to facilitate meaningful communication in these situations involve a departure from standard history taking. More research is required to better define the nature of challenges encountered in history taking with a view to develop better educational models for trainee physicians.
15 Apr 2026
Read appraisal →JMIR medical education
Developing and Validating a Coding Scheme for Clinical Reasoning in History Taking Using Generative AI-Based Virtual Patients: Systematic Text Condensation Approach
BACKGROUND: Effective history taking helps clinicians identify key symptoms and form accurate hypotheses. Generative artificial intelligence (GenAI)-based virtual patients (VPs) are increasingly used to simulate and practice history taking. However, there is currently no straightforward approach to effectively identify students' clinical reasoning activities during these interactions, which limits the ability to provide instructional feedback. OBJECTIVE: This study aims to develop and validate a coding scheme to identify medical students' history-taking behaviors during interactions with GenAI-based VPs. METHODS: Second-year medical students (N=210) participated in 5 history-taking cases with GenAI-based VPs, yielding 1030 dialogues. Researchers applied the systematic text condensation method to these dialogue data from cases 1 to 4 to inductively develop a coding scheme and validate coding consistency. Subsequently, the dialogue data from case 5 were used to assess the correlation between the students' history-taking behaviors and their academic performance, including diagnostic accuracy, history-taking checklist scores, clinical knowledge test scores, and postencounter form scores. RESULTS: A coding scheme comprising 12 behaviors across 3 dimensions-clinical reasoning behaviors, information gathering behaviors, and social interaction behaviors-was developed with high interrater reliability (κ≥0.85). The correlation analysis revealed that key clinical reasoning behaviors, such as summarizing and integrating and logical organization, showed significant positive correlations with multiple performance metrics, underscoring their importance in fostering clinical competence. In contrast, information gathering behaviors such as specifying symptoms and routine question were associated with clinical knowledge and thoroughness of history taking but were less predictive of diagnostic accuracy. CONCLUSIONS: This study developed a reliable, theory-informed coding scheme that can identify students' questioning behaviors during history taking with GenAI-based VPs. The scheme effectively captures higher-order cognitive strategies and provides valuable insights into the development of clinical reasoning in medical students. This approach offers a scalable and efficient way to integrate real-time feedback into future medical education, fostering personalized learning and advancing competency-based assessments in clinical training.
14 Apr 2026
Read appraisal →Medical education online
AI-assisted learning: ChatGPT for anamnesis in women's health education
UNLABELLED: Artificial intelligence (AI) tools such as ChatGPT are rapidly being adopted in medical education, yet evidence regarding their impact on learners' history-taking (anamnesis) skills remains limited. This gap is particularly relevant in women's health education, where high-quality anamnesis is essential for addressing sensitive, context-dependent concerns and for ensuring the safety of two interrelated patients: mother and fetus. METHODS: This study comprised two phases. First, semi-structured virtual interviews were administered to students and faculty to assess their familiarity with ChatGPT and access to digital technologies. Second, an in-person classroom intervention was implemented within the Women's Health discipline, integrating ChatGPT into history-taking instruction. Students' anamnesis performance was evaluated before and after ChatGPT-assisted learning. Additionally, a novel instructional activity was introduced in which ChatGPT generated cases classified as plausible, intermediate, or physiologically impossible, serving as prompts for guided discussion focused on diagnostic pitfalls, critical appraisal, and clinical reasoning. RESULTS: In the virtual phase, faculty (n = 62) and students (n = 115) completed the questionnaires. The classroom intervention involved 40 medical students participating as part of routine Women's Health teaching activities, with faculty oversight to ensure pedagogical alignment and responsible AI use. A pronounced discrepancy emerged between students' familiarity with ChatGPT (99.1%) and lower levels of familiarity among faculty. Anamnesis performance improved significantly after ChatGPT-assisted learning (p < 0.0001). Students reported increased confidence and perceived enhancement of history-taking quality, and all participants viewed ChatGPT as a valuable educational resource. The AI-generated cases effectively stimulated discussion and reinforced clinical reasoning processes. Findings contributed to the development of a practical guideline for the responsible integration of ChatGPT into Women's Health education. CONCLUSIONS: ChatGPT can enhance anamnesis and promote critical reasoning through structured interaction with AI-generated cases. However, its effective and responsible use depends on targeted faculty development to ensure alignment with pedagogical goals, appropriate oversight, and consistent integration into curriculum.
5 Apr 2026
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