Level Of Expertise Research Articles

Online Social Influence and Negative Emotions toward Snow Sports Brands: Moderation and Mediation Effects

This article draws on the stimulus–organism–response (SOR) model to understand the role of negative emotions in the anti-brand behaviors of online users who consume snow sports brands. To this end, both the online social influence and the mediating effect of symbolic incongruence (stimulus) on the generation of negative emotions (anger, stress, frustration, fear, boredom and sadness) (organism), and how these influence the formation of negative customer brand engagement (nCBE) (response), are analyzed. The study also analyses the moderating effects of “level of expertise”, this makes it possible to capture differences in behaviors based on the profile of the users in each of the proposed relationships. Questionnaire responses of 400 ski and snowboard users over 18 years of age were analyzed using a quantitative methodology. The results obtained have important theoretical and practical implications, since they confirm that online social influences have both a direct and indirect (mediating) effect on negative emotions, which positively affects the nCBE of online users of snow sports brands. Significant differences in behavior based on experience level (moderation effect) were also found. The study proposes useful practical recommendations applicable in online environments that the extreme sports industry could use to neutralize/avoid highly detrimental consequences.

A Study to assess the Effectiveness of planned Teaching Programme on knowledge regarding specific Learning Disabilities in children among Primary School Teachers at selected Schools, Dharwad District Karnataka, India

Nowadays children are day after today’s citizens. they're in a non-stop system of growth and improvement. Any alteration in its direction leads to developmental problems. specific studying incapacity plays a considerable position as silent handicap amongst children. it is expected that four-5% of college students in primary faculty have unique mastering disability. unique studying disability is “A disorder that affect people’s potential to either interpret what they see and listen or to link records from exceptional components of the brain. Such problems volume to high school work and may impend learning to study, write or mathematic’’. As there aren't any precise test to discover kids with precise studying incapacity, fitness specialists must rely mainly on number one faculty teachers report for its prognosis. preceding research have proved that trainer’s understanding towards such youngsters have exquisite influence closer to their restoration. An evaluative study turned into carried out 60 primary faculty teachers at decided on schools, Dharwad district. opportunity; simple random sampling method become used to choose the pattern and become collected through based know-how questionnaire. The studies design used for the look at quasi-experimental: unmarried organization pre-check put up-test design. records evaluation changed into achieved by means of using descriptive and inferential information. universal result of the observe revealed that the extent of knowledge on unique mastering disabilities in youngsters in pre-test revealed that the pre-take a look at suggests that Majority 30(50%) of the respondents had insufficient level of expertise and followed by 18(30%) of the respondents had moderate level of information and Minority 12(20%) had adequate expertise. all through the publish-take a look at indicates that Majority 36 (60%) of the respondents had good enough degree of knowledge and accompanied through 14(23.33%) of the respondents had mild degree of know-how and Minority 10(16.66%) had insufficient knowledge. calculated paired ‘t’ fee (t cal = 20.91) is more than tabulated price (t tab= 2.0096). there may be no considerable affiliation between selected socio-demographic variables. The look at concluded that the deliberate coaching programme was powerful in enhancing know-how concerning specific studying disabilities.

Reviewer Experience Detecting and Judging Human Versus Artificial Intelligence Content: The Stroke Journal Essay Contest.

Artificial intelligence (AI) large language models (LLMs) now produce human-like general text and images. LLMs' ability to generate persuasive scientific essays that undergo evaluation under traditional peer review has not been systematically studied. To measure perceptions of quality and the nature of authorship, we conducted a competitive essay contest in 2024 with both human and AI participants. Human authors and 4 distinct LLMs generated essays on controversial topics in stroke care and outcomes research. A panel of Stroke Editorial Board members (mostly vascular neurologists), blinded to author identity and with varying levels of AI expertise, rated the essays for quality, persuasiveness, best in topic, and author type. Among 34 submissions (22 human and 12 LLM) scored by 38 reviewers, human and AI essays received mostly similar ratings, though AI essays were rated higher for composition quality. Author type was accurately identified only 50% of the time, with prior LLM experience associated with improved accuracy. In multivariable analyses adjusted for author attributes and essay quality, only persuasiveness was independently associated with odds of a reviewer assigning AI as author type (adjusted odds ratio, 1.53 [95% CI, 1.09-2.16]; P=0.01). In conclusion, a group of experienced editorial board members struggled to distinguish human versus AI authorship, with a bias against best in topic for essays judged to be AI generated. Scientific journals may benefit from educating reviewers on the types and uses of AI in scientific writing and developing thoughtful policies on the appropriate use of AI in authoring manuscripts.

Reliability testing of sonographic techniques in detecting lateral displacement of patellar tendon

ObjectivesPatellofemoral pain syndrome (PFPS), a common knee condition, is characterized as presence of peripatellar pain during activities especially during loading of the lower extremity. Gonzalez-Suarez et al. (2021) developed a sonographic technique that assessed patellar tendon displacement within the trochlear groove. This study aims to determine the reliability of this sonographic technique. MethodsIntra-rater and inter-rater reliability study of three sonologist on the lateral displacement of the patellar tendon using ultrasound. Patellar tendon displacement was assessed using the three novel sonographic measurement techniques: 1) region of trochlear groove where the patellar tendon midpoint is located, 2) distance between deepest portion of the trochlear groove and patellar tendon midpoint and 3) percentage of lateral displacement of the patellar tendon. The measurements were done with the patients in supine and standing. ResultsTen participants with a mean age of 27.6 ± 1.56 years were included in the study. Pilot studies were done to improve the sonographic technique. Intraclass correlation coefficients yielded good to excellent inter-rater and excellent intra-rater reliability (>0.75) in the supine and standing positions except the inter-rater reliability of the region of the trochlear groove where patellar tendon midpoint is in supine position. ConclusionThe sonographic techniques for measurement of lateral patellar tendon displacement showed good to excellent intra-rater and inter-rater reliability between sonologists of different levels of expertise using a well-defined scanning protocol.

Validation and efficacy of the Varix Trainer model as a Training device for esophagogastroduodenoscopy.

Background and study aims The Varix Trainer model 1 (VTM1) was created for trainees to safely practice basic endoscope manipulation skills. The VTM1 was tested to see if it could distinguish levels of endoscope manipulation skills (construct validity) and whether training with it could improve these skills faster (content validity). Patients and methods We enrolled 23 novice endoscopists, 18 second-year trainees, and 13 expert endoscopists. They were asked to point with the endoscope tip to 20 numbers in the model as quickly as possible using torque, single-hand small/large wheel manipulation (SHSW), and retroflexion techniques. Their mean times (t20) were compared to determine if the model could distinguish different levels of expertise. Subsequently, 14 novices trained for eight short sessions, and the pre-training and post-training t20 were compared. Nine novice endoscopists received no training and were retested after 4 to 6 weeks (controls). Results Experts had faster t20 than second-year trainees, who were faster than novices, for all three techniques ( P < 0.001). After eight sessions, the mean t20 for novices improved from 112 to 66 seconds for torque, 144 to 72 seconds for SHSW, and 108 to 63 seconds for retroflexion, (all P < 0.001). Their t20 were equivalent to second-year trainees. Improvement in t20 was also seen with the control group, but total reduction was less than for the training group. Conclusions The VTM1 distinguished varying levels of expertise for all techniques, suggesting that it is a valid tool for assessing endoscope manipulation skill. A short curriculum improved novices' manipulation skills faster than traditional practice.

The year long journey: Transforming the BSN-DNP curriculum

BackgroundRevising a doctor of nursing practice (DNP) curriculum for family nurse practitioner and psychiatric mental health nurse practitioner education with an eye on future changes in competencies required planning and consensus among taskforce members. Major priorities included: the need to embed competency- based education (CBE), the National Taskforce Criteria for Nurse Practitioner (6th ed.) education, and the Essentials: Core competencies for professional nursing education (American Association of Colleges of Nursing (AACN) Essentials, 2021). FindingsChallenges began with faculty knowledge of CBE, NP faculty skills in curriculum building, buying in to the revised degree, and reviewing all current literature on the DNP NP knowledge, skills and attitudes needed for current practice. The year long journey yielded a comprehensive curriculum model to share as well as several lessons learned and recommendations for this journey. ConclusionThe process resulted in a curriculum revision that thus far, is better suited to development of the “work-ready” graduate while still supporting individualized student needs in attending a doctoral-level program. Ongoing work is focused on specific “pillars” of program support including clinical preceptor placement and partnerships, preceptor resources, student retention resources, and exploration of competency-based tracking data management.Considering the impact on faculty workload, program leadership should actively assess levels of curricular expertise of faculty involved to determine additional development or training needs for individual professional growth.

LDCNN: A new arrhythmia detection technique with ECG signals using a linear deep convolutional neural network.

The electrocardiogram (ECG) is a fundamental and widely used tool for diagnosing cardiovascular diseases. It involves recording cardiac electrical signals using electrodes, which illustrate the functioning of cardiac muscles during contraction and relaxation phases. ECG is instrumental in identifying abnormal cardiac activity, heart attacks, and various cardiac conditions. Arrhythmia detection, a critical aspect of ECG analysis, entails accurately classifying heartbeats. However, ECG signal analysis demands a high level of expertise, introducing the possibility of human errors in interpretation. Hence, there is a clear need for robust automated detection techniques. Recently, numerous methods have emerged for arrhythmia detection from ECG signals. In our research, we developed a novel one-dimensional deep neural network technique called linear deep convolutional neural network (LDCNN) to identify arrhythmias from ECG signals. We compare our suggested method with several state-of-the-art algorithms for arrhythmia detection. We evaluate our methodology using benchmark datasets, including the PTB Diagnostic ECG and MIT-BIH Arrhythmia databases. Our proposed method achieves high accuracy rates of 99.24% on the PTB Diagnostic ECG dataset and 99.38% on the MIT-BIH Arrhythmia dataset.

Why trust scientists?

Wonder implies the desire to learn. Aristotle A number of surveys, conducted in the aftermath of COVID19, have found that levels of trust in scientists are moderately high worldwide, although there are significant differences between countries. As the largest global survey reveals, scientists are perceived to have a high level of expertise, moderate integrity and benevolent intentions. Although with a different research focus, the 2024 edition of the Edelman Trust Barometer also found that trust in scientists is high relative to government leaders or CEOs, making them expected to lead the implementation of innovations.

Refining sleep staging accuracy: Transfer learning coupled with scorability models.

This study aimed to 1) improve sleep staging accuracy through transfer learning, to achieve or exceede human inter-expert agreement; 2) introduce a scorability model to assess the quality and trustworthiness of automated sleep staging. A deep neural network (base model) was trained on a large multi-site polysomnography (PSG) dataset from the United States. Transfer learning was used to calibrate the model to a reduced montage and limited samples from the Korean Genome and Epidemiology Study (KoGES) dataset. Model performance was compared to inter-expert reliability among three human experts. A scorability assessment was developed to predict the agreement between the model and human experts. Initial sleep staging by the base model showed lower agreement with experts (κ=0.55) compared to inter-expert agreement (κ=0.62). Calibration with 324 randomly sampled training cases matched expert agreement levels. Further targeted sampling improved performance, with models exceeding inter-expert agreement (κ=0.70). The scorability assessment, combining biosignal quality and model confidence features, predicted model-expert agreement moderately well (R²=0.42). Recordings with higher scorability scores demonstrated greater model-expert agreement than inter-expert agreement. Even with lower scorability scores, model performance was comparable to inter-expert agreement. Fine-tuning a pre-trained neural network through targeted transfer learning significantly enhances sleep staging performance for an atypical montage, achieving and surpassing human expert agreement levels. The introduction of a scorability assessment provides a robust measure of reliability, ensuring quality control and enhancing the practical application of the system before deployment. This approach marks an important advancement in automated sleep analysis, demonstrating the potential for AI to exceed human performance in clinical settings.

Discourses in Swedish preschool music teachers’ conversations: proficiency, subordination and resistance

ABSTRACT In recent years, Swedish preschool has been subjected to significant changes. As there is a lack of studies examining how teachers in music rhetorically position themselves within this context, the aim is to study subject positions that are constructed in conversations between preschool music teachers (PMTs) and to discuss these positions in relation to the quality of preschool practice in music education. The theoretical and methodological framework is grounded in social constructionist theories and the empirical material consists of group conversations with PMTs. The results reveal three prominent discourses, the discourses of Proficiency, of Subordination and of Resistance. In conclusion, the PMTs show that they are opposed by other pedagogues in various ways. This makes the possibility of establishing a music-teaching practice on their level of expertise severely restricted, which in turn has an impact on the quality of the preschool practice in music education.

Sexuality educators’ perceptions of and experiences with sexuality certification

ABSTRACT Sexuality education is often implemented by individuals of various backgrounds and training which may cause doubt about the level of competence and expertise provided. Certification can be a strategy to gain knowledge and skills and distinguish oneself in the field; however, little is known about sexuality educators’ experience of certification. This mixed-methods study sought to address this gap in the literature. Sexuality educators (n = 157) were asked about their experiences of certification and the perceived value of sexuality education and sexology certifications. Findings indicate a variety of certifications (n = 32) held by sexuality educators with significant differences in holding/pursuing a certification based on the highest degree earned, the geographical region worked in, and the work setting. Respondents perceived sexology certification as less valuable than sexuality education certification, and those with bachelor’s degrees valued sexuality education certification more than those with master’s and doctoral degrees. Cost and time were common barriers to certification, and individuals choosing not to pursue certification questioned its value for their job or success in the field. These results provide guidance to organisations offering certification and can also provide insight for sexuality educators as they consider certification as part of their professional journey.

Analysis of adenoma detection rate of colonoscopy among trainees.

To analyze adenoma detection rate (ADR) and related quality indicators of colonoscopy among trainees and make recommendations for appropriate colonoscopy training. ADR and related indicators of colonoscopies performed by 3 trainees and 5 colonoscopy experts between March and November 2022 were analyzed. These indicators were analyzed in both the entire patients and the screening/surveillance group. In addition, the training period of the 3 trainees was divided into 3 sections, and the changes in these indicators were examined. The mean ADR of the 3 trainees was 50.6%. In the screening/surveillance group, the mean ADR of the 3 trainees was 51.8%, showing no significant difference from the experts' ADR (53.4%). When the training period was divided into 3 sections and analyzed in the screening/surveillance group, the mean ADR of the trainees gradually increased to 49.4%, 52.6%, and 53.6%, respectively; however, the difference was insignificant. Analyzing each trainee's ADR, there was a significant difference among the 3 trainees (58.5% vs. 44.7% vs. 50.2%, P=0.008). However, in the third section of the training period, the 3 trainees' ADRs were 53.0%, 49.2%, and 57.3%, respectively, showing no significant difference (P=0.606). In the early stages of training, the ADR was higher than recommended; however, there were variances in ADR between individuals. As the training period passed, the ADR became similar at the expert level, whereas the difference in ADR between trainees decreased. Therefore, efforts to increase ADR should be made actively from the beginning of training and continued during the training period.

Adding texture to the Art of constraints-led coaching: a request for more research-informed guidelines

ABSTRACT The constraints-led approach has become an increasingly popular framework for developing functionally adaptive movement skills among athletes. Despite this popularity, the application of the constraints-led approach requires a level of conceptual, theoretical, and practical expertise, which we believe is in need of further applied research. In this paper, we aim to challenge constraints-led researchers and practitioners to provide more research-informed guidelines to support coaches who wish to become ecological designers. In doing so, we first discuss the underlying theoretical perspectives, providing a balanced account of the possible strengths and limitations of the ecological dynamics and constraints-led approaches. We then pose three questions we believe have yet to receive the empirical attention necessary to help coaches become effective ecological designers. In posing these questions, we hope to encourage greater empirical guidance about the application of the constraints-led approach in situations coaches routinely encounter in the daily training and competition environment.

Comparative Efficacy and Safety of Robot-Assisted vs. Freehand Screw Placement in Femoral Neck Fractures: An Updated Systematic Review and Meta-Analysis.

Background: Femoral neck fractures pose significant surgical challenges with high morbidity and mortality. Traditional freehand screw placement often yields variable outcomes. Recent robotic advancements offer a promising alternative with enhanced precision. Methods: This systematic review compares the efficacy and safety of robot-assisted versus freehand techniques. A comprehensive literature search across multiple databases up to July 2024 included studies comparing both techniques. Primary outcomes were the union rate and time, functional outcomes, operative time, intraoperative parameters, and complication rates. Meta-regression analyses identified treatment response determinants. Results: Twenty-four studies (1437 patients) were included. Robot-assisted screw placement significantly improved the union rate, reduced the union time, and showed superior functional outcomes. Additionally, it resulted in shorter operative times, less intraoperative blood loss, and fewer instances of fluoroscopy and guide pin insertion. The risk of femoral neck necrosis was notably lower with robotic assistance. Meta-regression highlighted the robot type, patient age, and sample size as significant factors. Conclusions: Despite the promise of robot-assisted screw placement, limitations exist. The evidence being mainly from China raises concerns about generalizability. The lack of long-term follow-up data hinders assessment of technique durability. Unreported surgeon expertise levels and learning curves affect result validity. High initial costs and steep learning curves of robotic systems also present barriers to widespread adoption.

Cologne ergonomic measurement for robotic surgery (CEMRobSurg) using the Hugo™ RAS System.

The ergonomic advantages and potential challenges that robotic surgery poses to the well-being of surgeons are mainly unexplored. The most recent surgical robot introduced on the European market is the Hugo™ RAS System by Medtronic. This study aims to evaluate the ergonomic benefits of the Hugo™ RAS System, which is available in our training laboratory, CeMIT (Center for Medical Innovation and Technology Cologne). Using the previously established Cologne Ergonomic Measurement Setup for Robotic Surgery (CEMRobSurg), we measured three parameters related to ergonomic posture from subjects with different levels of surgical expertise (laypeople, medical students, surgical residents, and expert robotic surgeons). The heart rate was measured continuously using a polar band. The noise level was measured while using the Hugo™ RAS System, and automated photographs using our locally developed methodology were captured of the participant every 2s to assess body posture. The ergonomic measurements were conducted while the subject performed the same standardized robotic training exercises (Peg Board, Rope Walk, and Ring Walk). A total of 53 participants were enrolled in this study. The average noise level during all measurements was 54.87 dB. The highest stress level was measured in surgical residents with a sympathetic nervous system index (SNS index) of 1.15 (min - 1.43, max 3.56). The lowest stress level was measured in robotic experts with an SNS index of 0.23 (min - 0.18, max 0.91). We observed a risk-prone positioning of the neck and elbow in medical students (mean 39.6° and 129.48°, respectively). Robotic experts showed a risk positioning in the knee and hip region (mean 107.89° and 90.31°, respectively). This is the first study to analyze and objectify the ergonomic posture of medical students, surgical trainees, surgeons, and laypeople using the open console, modular Hugo™ RAS System. Our findings offer recommendations for operating surgeons and allow for a comparative analysis between the different robotic systems. Further evaluations in real-time operative scenarios will follow.

The Treatment of Metastatic Renal Cell Carcinoma.

Approximately 15 000 people receive a diagnosis of renal cell carcinoma (RCC) in Germany each year; in 20-30% of cases, metastatic RCC (mRCC) is already present at the time of diagnosis. This disease in the metastatic stage is still mainly treated palliatively, yet the multimodal therapeutic landscape has changed markedly over the past 15 years, with the approval of many new treatments for patients with mRCC. This review is based on prospective studies retrieved by a selective search in PubMed and the ASCO and ESMO databases and on the German and European oncological and urological guidelines for RCC. Drugs are the mainstay of treatment. mRCC can be treated with a combination of two immune checkpoint inhibitors (CPIs), a CPI and a tyrosine-kinase inhibitor (TKI) (evidence level IA), or a TKI as monotherapy (evidence level IIC-IC). With prognosis-based sequential drug treatment, a mean progression-free survival of 12 to 24 months and an overall survival of approximately 50 months can be achieved from the time of initiation of first-line therapy. Aside from pharmacotherapy, the multidisciplinary tumor board should evaluate the indications for local treatments such as cytoreductive nephrectomy, metastasectomy, and radiotherapy, depending on the individual prognostic constellation and the patient's present condition. Optimal individualized decisions require a high level of expertise and the collaboration of a multidisciplinary tumor board. Older prognostic parameters currently play a leading role in decision-making, while predictive parameters and molecular markers are not yet adequately validated.

Feasibility and Educational Value of Clinical Cases Generated Using Large Language Models.

In medical education, case-based learning (CBL) is a fundamental method for training healthcare professionals across different levels of expertise. This approach hinges on using authentic or fabricated clinical cases to bridge the gap between theoretical knowledge and its practical application. It fosters active engagement and knowledge application among learners in healthcare domains. While creating effective cases demands substantial clinical understanding and time investment, the integration of Generative Artificial Intelligence (AI) presents a promising solution to this challenge. AI can efficiently analyze extensive medical data to generate diverse and realistic clinical scenarios, continuously updating case content based on emerging medical literature and guidelines. This study explores AI-generated cases' feasibility and educational value in continuing medical education, focusing on COVID-19 scenarios tailored for the MENA region. Results indicate the potential of AI-generated cases to foster engagement and critical thinking among learners, suggesting their suitability for different levels of education. This study highlights the advantages of integrating AI into CBL and emphasizes the need for future efforts to tackle obstacles and facilitate its successful adoption.

Priorities and barriers for research related to primary ciliary dyskinesia.

Despite advances in primary ciliary dyskinesia (PCD) research, many questions remain; diagnosis is complex and no disease-specific therapies exist. Using a mixed-methods approach, we aimed to identify priorities for clinical and epidemiological research and explore barriers to research. To obtain rich, relevant, diverse data, we performed in-depth semi-structured interviews with PCD specialists selected using purposive sampling. We transcribed, coded and analysed interview data using thematic analysis. Based on interview themes that we identified, we developed an anonymous survey and circulated it widely through the BEAT-PCD network. We interviewed 28 participants from 15 countries across different disciplines and expertise levels. The main themes identified as priorities for PCD research were improving diagnosis; understanding prevalence and disease course; phenotypic variability; disease monitoring; treatment strategies; clinical trial end-points; and poorly researched areas. In total, 136 participants (49% paediatric pulmonologists) from 36 countries completed the survey. Most commonly reported barriers for research were low awareness about PCD and difficulties securing funding - in more than one-third of cases, participants reported undertaking predominantly unfunded research. Research questions ranked highest included priorities related to further improving diagnosis, treating PCD, managing upper and lower airway problems, and studying clinical variability and disease prognosis. We need to overcome barriers of limited funding and low awareness and promote collaborations between centres, disciplines, experts and patients to address identified PCD priorities effectively. Our results contribute to the ongoing efforts of guiding the use of existing limited research resources and setting up a roadmap for future research activities.

Machine learning allows expert level classification of intraoperative motor evoked potentials during neurosurgical procedures

ObjectiveTo develop and evaluate machine learning (ML) approaches for muscle identification using intraoperative motor evoked potentials (MEPs), and to compare their performance to human experts. BackgroundThere is an unseized opportunity to apply ML analytic techniques to the world of intraoperative neuromonitoring (IOM). MEPs are the ideal candidates given the importance of their correct interpretation during a surgical operation to the brain or the spine. In this work, we develop and test a set of different ML models for muscle identification using intraoperative MEPs and compare their performance to human experts. In addition, we provide a review of the available literature on current ML applications to IOM data in neurosurgery. MethodsWe trained and tested five different ML classifiers on a MEP database developed from six different muscles in patients who underwent brain or spinal cord surgery. MEPs were obtained by both transcranial (TES) and direct cortical stimulation (DCS) protocols. The models were evaluated within a single patient and on previously unseen patients, considering signals from TES and DCS both independently and mixed. Ten expert neurophysiologists classified a set of 50 randomly selected MEPs, and their performance was compared to the best performing model. ResultsA total of 25.423 MEPs were included in the study. Random Forest proved to be the best performing model with 99 % accuracy in the single patient dataset task and a 78 %–94 % accuracy range on previously unseen patients. The model performance was maximized by representing MEPs as a set of features typically employed in signal processing compared to traditional neurophysiological parameters. The classification ability of the Random Forest model between six different muscles and across different MEP acquisition modalities (79 %) significantly exceeded that of human experts (mean 48 %). ConclusionsCarefully selected ML models proved to have reliable capacity of extracting meaningful information to classify intraoperative MEPs using a limited number of features, proving robustness across patients and signal acquisition modalities, outperforming human experts, and with the potential to act as decision support systems to the IOM team. Such encouraging results lay the path to further explore the underlying nature of clinically important signals, with the aim to continue to produce useful applications to make surgeries safer and more efficient.

GPT-4o’s competency in answering the simulated written European Board of Interventional Radiology exam compared to a medical student and experts in Germany and its ability to generate exam items on interventional radiology: a descriptive study.

This study aimed to determine whether ChatGPT-4o, a generative artificial intelligence (AI) platform, was able to pass a simulated written European Board of Interventional Radiology (EBIR) exam and whether GPT-4o can be used to train medical students and interventional radiologists of different levels of expertise by generating exam items on interventional radiology. GPT-4o was asked to answer 370 simulated exam items of the Cardiovascular and Interventional Radiology Society of Europe (CIRSE) for EBIR preparation (CIRSE Prep). Subsequently, GPT-4o was requested to generate exam items on interventional radiology topics at levels of difficulty suitable for medical students and the EBIR exam. Those generated items were answered by 4 participants, including a medical student, a resident, a consultant, and an EBIR holder. The correctly answered items were counted. One investigator checked the answers and items generated by GPT-4o for correctness and relevance. This work was done from April to July 2024. GPT-4o correctly answered 248 of the 370 CIRSE Prep items (67.0%). For 50 CIRSE Prep items, the medical student answered 46.0%, the resident 42.0%, the consultant 50.0%, and the EBIR holder 74.0% correctly. All participants answered 82.0% to 92.0% of the 50 GPT-4o generated items at the student level correctly. For the 50 GPT-4o items at the EBIR level, the medical student answered 32.0%, the resident 44.0%, the consultant 48.0%, and the EBIR holder 66.0% correctly. All participants could pass the GPT-4o-generated items for the student level; while the EBIR holder could pass the GPT-4o-generated items for the EBIR level. Two items (0.3%) out of 150 generated by the GPT-4o were assessed as implausible. GPT-4o could pass the simulated written EBIR exam and create exam items of varying difficulty to train medical students and interventional radiologists.