Clinical Research Directory

End of Term vs. End of Module Examinations in the Preclinical Stage: A Cross-Sectional Survey of Student and Faculty Perspectives

Current practice and debate: Many medical schools use hybrid models that combine modular and final exams to balance reliability, validity, and feasibility. Still, there is no consensus on the optimal structure. The debate centers on three issues: educational impact, student stress, and faculty workload. Modular systems may support continuous learning but raise concerns about assessment burden and fragmentation of knowledge, as students may learn topics in isolation rather than as an integrated whole. End-of-term exams, by contrast, risk encouraging short-term memorization even though they test integration more effectively. Justification for the study: Given these competing advantages and drawbacks, the study aims to explore the perspectives of both students and faculty on the two approaches. Understanding how each stakeholder group perceives the effects on learning, stress, workload, and performance can fill a gap in the existing literature, which focuses mainly on learning behaviors rather than stakeholder views. Expected contribution: The findings are intended to inform evidence-based decisions on assessment design in preclinical medical education. By clarifying the trade-offs between distributed and cumulative exams, the study seeks to help institutions optimize learning outcomes while keeping assessment workload and student stress manageable

Title of Study: Anesthesiologists' Learning Curves for Ultrasound Assessment of Difficult Airway

This study aims to determine how many supervised practice attempts anesthesia trainees need to become competent in using ultrasound to assess the airway. Airway ultrasound may help clinicians identify anatomical features associated with a difficult airway, which can make breathing support or placement of a breathing tube more challenging during anesthesia. However, the amount of training needed to perform these ultrasound measurements accurately has not been established. Eight anesthesia trainees will receive theoretical instruction and hands-on training before performing ultrasound assessments on 20 healthy adult volunteers. The trainees will measure tongue thickness and the distances from the skin to the epiglottis and vocal cords. Their measurements will be compared with those of experienced airway ultrasound experts to assess accuracy and determine how many attempts are needed to achieve competency. A follow-up assessment three months later will evaluate whether the trainees retain their skills. The study hypothesis is that anesthesia trainees can achieve competency in airway ultrasound assessment after a limited number of supervised practice attempts and retain these skills over time. The findings may help establish evidence-based training requirements for airway ultrasound assessment.

An Automated Virtual Reality Intervention to Enhance Firearm Safety Counseling in Pediatrics

Clinicians will complete a screen-based automated virtual reality training intervention to learn how to counsel caregivers of pediatric patients on secure firearm storage.

Generative AI-Assisted Personalized Feedback for Laparoscopic Skill Learning in Medical Undergraduates

Laparoscopic training involves learning to control surgical instruments while viewing movements on a screen. Feedback can help learners identify technical problems and plan further practice, but providing individualized guidance can require substantial instructor time. This study compares personalized feedback assisted by generative artificial intelligence (AI) with feedback produced using predefined rules and fixed statements. The purpose is to determine whether AI assistance provides additional benefits for laparoscopic skills learning. The investigators hypothesize that AI-assisted personalized feedback will help medical students improve laparoscopic skills more than structured rule-based feedback.

Comparison of Task Trainer and Virtual Reality Approaches in Dysrhythmia Management Education

The aim of this randomized controlled simulation-based educational study was to compare the effectiveness of virtual reality (VR)-based and task trainer-based training in improving dysrhythmia management performance among intern physicians. The study addressed two main research questions: 1. Is VR-based training as effective as task trainer-based training in improving dysrhythmia management performance? 2. Are improvements in dysrhythmia management performance maintained four weeks after training? A total of 30 intern physicians participated in the study, with 15 participants assigned to each group. Participants underwent the following procedures: * A baseline performance assessment using a high-fidelity patient simulator involving bradydysrhythmia, supraventricular tachycardia, and ventricular tachycardia scenarios. * Practical dysrhythmia management training using either an immersive VR environment or a conventional task trainer. * An immediate post-training performance assessment using high-fidelity simulation scenarios. * A retention assessment four weeks after training to evaluate the maintenance of acquired skills. Participant performance was evaluated using standardized, weighted checklists based on dysrhythmia management algorithms. Performance scores ranged from 0 to 100, with higher scores indicating better performance. All assessments were video-recorded for subsequent evaluation. Changes in performance scores were compared between the two groups to evaluate the effectiveness of the educational interventions and the retention of acquired skills.

The Role of Simulation Fidelity in Early Surgical Skills Education

The aim of the study is to evaluate the effectiveness of teaching surgical suturing techniques using various materials (e.g., banana peel, orange peel, pork, chicken, and silicone pads). The study results will help identify the most effective teaching materials and methods, which will contribute to optimizing educational programs and improving the quality of training for future surgeons.

AI Smart Glasses Feedback for Intubation Training in Medical Students

The goal of this educational study is to learn whether AI smart glasses can score and give feedback on endotracheal intubation performed by medical students on a manikin, and when this AI feedback is most useful during anesthesia training. The main questions it aims to answer are: * Do AI scores of the intubation OSCE (Objective Structured Clinical Examination) agree with scores given by faculty examiners? * Does practicing with AI smart-glasses feedback before or after the airway lecture lead to better intubation OSCE performance, self-efficacy, and cognitive load? Medical interns rotating through the Department of Anesthesiology at Tri-Service General Hospital (Taiwan) for 2 weeks will be randomized by rotation group to practice intubation once with the AI smart glasses either before the airway lecture (week 1) or after it (week 2). At the end of the rotation, all participants will take an intubation OSCE while wearing the AI glasses, scored independently by a faculty examiner and by the AI. Participants will also complete short questionnaires on self-efficacy (before and after the rotation) and cognitive load (after AI practice). All procedures are performed on a manikin; no patients are involved.

Critical Appraisals at Play

Pre and Post assessment of knowledge and attitudes on critical appraisal of biomedical literature in Emergency Medicine practice in a cohort of residents with implementation of a competition based educational format.

AI-Generated and Rule-Based Individualized Feedback for Medical Students' Nephrology Learning

Medical education increasingly emphasizes individualized learning and formative assessment; however, providing timely and personalized feedback to students remains challenging because of limited faculty resources. Generative artificial intelligence (AI) may provide a scalable approach for generating individualized feedback based on students' learning needs, but its educational effectiveness compared with traditional feedback approaches remains uncertain. This study is a single-center, prospective, three-arm, cluster randomized controlled trial designed to evaluate the effect of pretest-based individualized feedback on medical students' learning outcomes during a nephrology clerkship. Undergraduate medical students participating in a nephrology clerkship at The Second Qilu Hospital of Shandong University will be recruited and assigned by teaching class clusters to one of three groups: an AI-generated feedback group, a rule-based individualized feedback group, or a no-feedback control group. All participants will receive the same nephrology clerkship teaching based on the BOPPPS-CBL instructional model. Before the clerkship, students will complete a diagnostic pretest. Based on the pretest results, individualized feedback will be provided according to group allocation. The primary outcome is immediate knowledge acquisition assessed by the post-test score immediately after the clerkship. Secondary outcomes include four-week knowledge retention, knowledge gap remediation, and clinical reasoning performance. This study aims to determine whether AI-generated individualized feedback provides additional educational benefits compared with traditional rule-based feedback and no feedback, and to provide evidence for the application of artificial intelligence-assisted personalized learning in medical education.

Intelligent Simulated Patient System for Gastroenterology Education

This randomized controlled trial evaluated a large language model-based Intelligent Simulated Patient System (ISPS) for gastroenterology history-taking education. Ninety medical students were randomly assigned in a 1:1 ratio to 4 weeks of conventional clinical learning or ISPS-assisted training. The ISPS used teacher-defined structured case information to support simulated patient interactions and generated item-level formative feedback using predefined scoring rubrics. The implemented system did not use external electronic health record retrieval, retrieval-augmented generation, a vector database, or semantic-similarity threshold scoring. The primary outcomes were post-intervention medical history-taking performance and clinical diagnostic accuracy. History-taking performance was assessed in a standardized Objective Structured Clinical Examination by two senior physicians who were blinded to group allocation. Secondary outcomes included patient-centered communication competence and participants' acceptance of the ISPS.

Teaching Medical Students to Verify AI-Generated Citation Hallucinations

Generative artificial intelligence (GenAI) tools are increasingly used by medical students for literature searching, scientific writing, and research. However, AI-generated citations may contain errors. Some references may not exist or may contain incorrect bibliographic details, while other references may be genuine but may not actually support the claim for which they are cited. This randomized educational study will evaluate whether a structured citation-verification framework called SARA can improve medical students' ability to identify and correct these problems. SARA teaches students to assess four elements of a citation: Source authenticity, Accuracy, Relevance, and Alignment between the cited evidence and the claim being made. Year 3-5 medical students will be randomly assigned to either a 75-minute SARA training session or an attention-matched session on responsible use of GenAI in academic work. Participants will complete citation-verification assessments before training, immediately after training, and 12 weeks later. The main outcome will assess whether students can independently apply the verification approach to an unstructured literature-review task 12 weeks after training, without being prompted to use SARA. The study will also examine which types of citation errors remain difficult, whether students initiate appropriate verification, the quality of their verification, handling of valid citations, and confidence in their judgments. A subgroup of participants will take part in think-aloud and follow-up interviews to explore how they approach citation verification and transfer the strategy to new tasks.

Teaching Doctors in Training to Reason With Artificial Intelligence

The purpose of the TEACH-AI study is to assess whether a brief, structured workshop on artificial intelligence can improve the performance of medicine doctors in training (i.e. residents) in their diagnostic and management reasoning. In this multi-site randomized controlled trial, internal medicine and family medicine residents are assigned either to receive an in-person workshop on safe, effective LLM use before a standardized AI-assisted assessment, or to complete the same assessment before receiving the workshop. Residents will review clinical cases that are fully synthetic, no protected health information is used, using a password protected LLM interface.

Emergency Structured Clinical Examination Training bOX

This study assessed whether a training toolbox called ECOX could help medical students prepare for their objective structured clinical examination (OSCE) during an emergency medicine clerkship. The hypothesis was that adding ECOX to usual training would improve examination performance and reduce examination-related anxiety. Eight 10-week clerkship rotations at Angers University Hospital, France, were randomly assigned to usual training alone or usual training plus ECOX, with four rotations in each group. All students in a rotation received the same assignment. ECOX provided a timer and reference assessment grids for practising seven-minute encounters with real patients who agreed to participate. Students could use the toolbox throughout their clerkship. Both groups completed the same three-station examination at the end of the clerkship. Examiners were unaware of group allocation. Anxiety was assessed at the beginning of the clerkship and immediately before the final examination.

Telemedicine Education Program for Medical Students

This study evaluated a structured telemedicine education program for undergraduate medical students. Third-year medical students participated in either a structured telemedicine education program or a control condition. The education program included theoretical sessions, observation of real telemedicine consultations, simulated patient encounters, and structured feedback. Students' telemedicine knowledge was assessed before and after the study, and their telemedicine consultation skills were evaluated through a standardized simulated patient encounter. The study aimed to examine whether structured telemedicine education could improve medical students' knowledge and consultation skills.

Effectiveness of AI-generated Flashcards for Enhancing Learning Outcomes in Undergraduate Biochemistry

This study evaluated the effectiveness of artificial intelligence (AI)-generated flashcards in improving learning outcomes among undergraduate medical students studying biochemistry. AI-generated flashcards were developed using generative AI tools and incorporated into teaching sessions on selected biochemistry topics. A prospective non-randomized crossover study was conducted among first-year Bachelor of Medicine and Bachelor of Surgery (MBBS) students at KMSK Government Medical College, Chandrapur, India. Participants received instruction through both AI-generated flashcards and conventional teaching methods in different study periods. Learning outcomes were assessed using pre-tests, post-tests, and delayed retention tests. Student perceptions regarding the usefulness, engagement, and acceptability of AI-generated flashcards were also evaluated. The study aimed to determine whether AI-generated flashcards could enhance immediate knowledge acquisition and long-term retention compared with conventional teaching methods in undergraduate biochemistry education.

Artificial Intelligence-Integrated BOPPPS Case-Based Learning for Nephrology Clerkship Education

This prospective, three-arm, parallel-group, cluster randomized controlled educational trial was conducted among third-year undergraduate medical students undertaking nephrology clerkship at The Second Hospital of Shandong University. The study aimed to compare the effects of lecture-based learning, BOPPPS combined with case-based learning, and artificial intelligence-integrated BOPPPS combined with case-based learning on students' theoretical knowledge, clinical reasoning, clinical case analysis, and critical appraisal of AI-generated clinical recommendations. Existing clinical clerkship teaching groups, rather than individual students, were randomized to one of three educational interventions: lecture-based learning (LBL), BOPPPS combined with case-based learning (BOPPPS-CBL), or artificial intelligence-integrated BOPPPS combined with case-based learning (AI-BOPPPS-CBL). Twelve teaching groups were randomized in a 1:1:1 ratio, with four teaching groups assigned to each arm. All groups received standardized instruction on acute kidney injury and chronic renal failure delivered by the same teaching team. The BOPPPS-CBL intervention incorporated structured learning activities including case discussion, progressive case disclosure, clinical reasoning exercises, and feedback. The AI-integrated group additionally used AI-generated clinical scenarios and diagnostic and treatment recommendations as materials for critical appraisal. Students identified potential errors or limitations in AI-generated content and proposed revisions based on textbooks, clinical guidelines, and evidence-based resources. The primary outcome was the clinical case analysis score. Secondary outcomes included theoretical test scores, knowledge gain, clinical reasoning score, AI critical appraisal score, and course feedback.

Virtual Reality for Teaching Abscess Incision and Drainage to Medical Students

This completed randomized educational trial evaluated whether virtual reality (VR)-assisted teaching improves medical students' performance in abscess incision and drainage compared with conventional teaching. A total of 120 second-year medical students were randomly assigned in a 1:1 ratio to either VR-assisted teaching or conventional teaching. Both groups received 120 minutes of instruction covering the same core learning objectives. The primary outcome was the immediate post-intervention objective structured clinical examination (OSCE) skills score for abscess incision and drainage. Secondary outcomes included theoretical knowledge, learning interest, and course satisfaction.

Digital Microlearning for Patient-Safety Readiness in Nursing Students

This randomized controlled trial evaluated whether a seven-day, patient-safety-focused digital microlearning program improved sustained clinical error recognition and the transfer of safety learning among second-year nursing students entering surgical clinical practice. Ninety students were randomly assigned in a 1:1 ratio to digital microlearning plus standard education or standard education alone. Outcomes were assessed at baseline, immediately after the intervention, at the end of the first week of surgical clinical practice, and at the end of the seventh week of surgical clinical practice. Weekly ecological momentary assessment prompts examined safety-oriented behavior during clinical practice. Blinded performance tasks assessed objective clinical transfer, and an explanatory qualitative component explored how and under what conditions students applied the learning in practice.

Integrated PBL-CBL-CTTM Teaching to Enhance Clinical Self-Efficacy in Endocrine Critical Illness Among Internal Medicine Residents: An RCT

This randomized controlled trial evaluates whether an integrated online-offline multimodal teaching approach combining Problem-Based Learning (PBL), Case-Based Learning (CBL), and Case Three-Dimensional Teaching Method (CTTM) improves clinical self-efficacy and hands-on proficiency in managing endocrine and metabolic critical illness among internal medicine residents, compared to traditional lecture-based learning (LBL). Ninety internal medicine residents on their first endocrine rotation will be randomly assigned in a 1:1:1 ratio to three groups: (1) Control group receiving traditional LBL alone; (2) Experimental group 1 receiving PBL plus LBL; (3) Experimental group 2 receiving the integrated PBL-CBL-CTTM closed-loop teaching program. All groups receive a standardized 12-hour curriculum on endocrine and metabolic critical illness, including diabetic ketoacidosis, hyperosmolar hyperglycemic syndrome, thyroid storm, and refractory hypoglycemia. The primary outcome is the clinical skills assessment score (0-50 points). Secondary outcomes include theoretical examination scores, General Self-Efficacy Scale (GSES) scores, a self-developed Endocrine High-Risk Management Self-Efficacy Source Questionnaire (four dimensions based on Bandura's self-efficacy theory), teaching satisfaction, and semi-structured focus group interviews. The study was conducted from September 2025 to August 2026 at the Fourth Affiliated Hospital of Anhui Medical University, Chaohu, China. This study aims to establish an evidence-based, replicable teaching model that addresses the gap between theoretical knowledge and clinical confidence in managing endocrine emergencies among residents.

Video-Based Versus Bedside eFAST Training in Final-Year Medical Students

This study compared two methods of teaching the Extended Focused Assessment with Sonography for Trauma (eFAST) examination to final-year medical students. Participants were randomly assigned in a 1:1 ratio to either a 60-minute standardized video-based training session or a 60-minute instructor-led bedside training session. Both groups received the same educational content, for the same duration, from the same instructor. Within one hour after completing the training, each participant performed an individual eFAST examination on a healthy volunteer. Performance was assessed by an evaluator who was unaware of the participant's training group, using an eFAST Image Acquisition Checklist. The study compared overall immediate eFAST performance between the two teaching methods and also examined performance in individual eFAST examination windows.

Gamification in Asynchronous Faculty Development

TEACH\_UP is a parallel-group randomized trial that evaluated whether adding a gamified design layer to a content-equivalent asynchronous faculty development course improved teaching self-efficacy, teaching orientation, engagement, course participation, and early teaching performance among medical educators at the University of Pécs Medical School. Participants were allocated to a gamified Moodle course or a standard Moodle course containing the same modules, learning objectives, assessments, and feedback. The gamified version added map-based progression, conditional release, experience points, badges, progress visibility, and a leaderboard. Outcomes included pre-post teaching self-efficacy and teaching orientation, post-course engagement, Moodle analytics, course performance, microteaching ratings, and a protocol-specified three-month follow-up survey.

An Agentic Co-educator System for Personalized Medical Education

With the rise of generative artificial intelligence and large language models, medical education is undergoing a significant transformation. Numerous studies have highlighted the limitations of traditional educational knowledge acquisition and the potential impact of artificial intelligence on medical education, resident training, and continuing education for clinical practitioners. However, there is a lack of real-world experiments on the effectiveness of AI-integrated education. Artificial intelligence can provide extensive educational resources and tools that are not limited by geographical location or language, thereby lowering the barrier to accessing high-quality medical education and promoting educational equity. Nevertheless, the performance of AI models trained by different medical institutions or healthcare systems may vary. To offer a more universal, accessible, high-quality, and interconnected educational journey. We have developed a virtual ophthalmology teacher, which developed based on foundational model and large language models. This model, named EyeTeacher provide comprehensive theoretical knowledge and clinical skills enhancement for untrained medical students. To verify the effectiveness of our EyeTeacher across different national ophthalmology education systems and languages, we plan to implement a randomized controlled trial. This trial will assess the clinical capabilities of all participants and explore the advantages and disadvantages of the system compared to traditional teaching methods.

Effect of a Community Service-Learning Intervention on Medical Empathy and Clinical Self-Efficacy in Medical Students

Medical empathy and clinical self-efficacy are key professional competencies that are difficult to develop through traditional classroom-based training alone. At the Faculty of Medicine of FES Iztacala (UNAM), the curriculum includes a community practice component in the Practica Clinica I module that is rarely implemented in practice, creating a gap between the formal and real curriculum. This quasi-experimental pre-post study evaluates the effect of a structured Service-Learning (SL) intervention - a community anthropometry and somatometry assessment session conducted at a primary school - on medical empathy, perceived clinical self-efficacy, and clinical report performance in 35 fourth-semester medical students. Medical empathy will be measured using the Jefferson Scale of Empathy, Student version (JSE-S), validated in Spanish for Latin American populations. Clinical self-efficacy will be measured using the Medical Self-Efficacy Scale (EAM), a 5-item Likert instrument developed by the principal investigator (Cronbach's alpha=0.818, McDonald's omega=0.862). Clinical performance will be assessed using a standardized 33-point rubric evaluated blindly by two independent faculty members, with inter-rater reliability calculated using the Intraclass Correlation Coefficient (ICC). Children participating in the community session will receive a personalized health report with their anthropometric results and, if clinically relevant findings are detected, will be referred to the University Health Clinic (CUSI) at FES Iztacala at no cost.

AI-Assisted Feedback for Learning Standardized Tuina Skills

The goal of this educational study is to determine whether personalized feedback generated using sensor data and generative artificial intelligence (AI) can improve the learning of standardized Tuina skills among rehabilitation trainees. Tuina is a form of manual therapy that requires learners to control the location, force, rhythm, and consistency of their hand movements. A total of 81 rehabilitation trainees will be randomly assigned to one of three training groups: AI-supported personalized feedback, sensor-based data feedback without AI-generated recommendations, or traditional instructor feedback. All groups will receive the same standardized demonstration, training tasks, practice duration, and number of practice sessions. The main question is whether trainees receiving AI-supported personalized feedback achieve better retention of standardized Tuina skills four weeks after training. The researchers will also compare immediate skill performance, force and rhythm control, transfer of skills to a related task, learning efficiency, self-efficacy, cognitive load, satisfaction, and the safety and acceptability of AI-generated feedback. Skill performance will be assessed using a blinded Objective Structured Clinical Examination (OSCE) and objective sensor-based measurements. The study activities will be conducted using a mechanical simulation model and a pressure-sensing system, rather than on patients.