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This study aimed to develop a deep learning model to assess the quality of fetal echocardiography and to perform prospective clinical validation. The model was trained on data from the 18-22-week anomaly scan conducted in seven hospitals from 2008 to 2018. Prospective validation involved 100 patients from two hospitals. A total of 5363 images from 2551 pregnancies were used for training and validation. The model's segmentation accuracy depended on image quality measured by a quality score (QS). It achieved an overall average accuracy of 0.91 (SD 0.09) across the test set, with images having above-average QS scoring 0.97 (SD 0.03). During prospective validation of 192 images, clinicians rated 44.8% (SD 9.8) of images as equal in quality, 18.69% (SD 5.7) favoring auto-captured images and 36.51% (SD 9.0) preferring manually captured ones. Images with above average QS showed better agreement on segmentations (p

Introduction: Simulation-based training (SBT) aids healthcare providers in acquiring the technical skills necessary to improve patient outcomes and safety. However, since SBT may require significant resources, training all skills to a comparable extent is impractical. Hence, a strategic prioritization of technical skills is necessary. While the European Training Requirements in Neonatology provide guidance on necessary skills, they lack prioritization. We aimed to identify and prioritize technical skills for a SBT curriculum in neonatology. Methods: A three-round modified Delphi process of expert neonatologists and neonatal trainees was performed. In round one, the participants listed all the technical skills newly trained neonatologists should master. The content analysis excluded duplicates and non-technical skills. In round two, the Copenhagen Academy for Medical Education and Simulation Needs Assessment Formula (CAMES-NAF) was used to preliminarily prioritize the technical skills according to frequency, importance of competency, SBT impact on patient safety, and feasibility for SBT. In round three, the participants further refined and reprioritized the technical skills. Items achieving consensus (agreement of ≥75%) were included. Results: We included 168 participants from 10 European countries. The response rates in rounds two and three were 80% (135/168) and 87% (117/135), respectively. In round one, the participants suggested 1964 different items. Content analysis revealed 81 unique technical skills prioritized in round two. In round three, 39 technical skills achieved consensus and were included. Conclusion: We reached a European consensus on a prioritized list of 39 technical skills to be included in a SBT curriculum in neonatology., INTRODUCTION: Simulation-based training (SBT) aids healthcare providers in acquiring the technical skills necessary to improve patient outcomes and safety. However, since SBT may require significant resources, training all skills to a comparable extent is impractical. Hence, a strategic prioritization of technical skills is necessary. While the European Training Requirements in Neonatology provide guidance on necessary skills, they lack prioritization. We aimed to identify and prioritize technical skills for a SBT curriculum in neonatology.METHODS: A three-round modified Delphi process of expert neonatologists and neonatal trainees was performed. In round one, the participants listed all the technical skills newly trained neonatologists should master. The content analysis excluded duplicates and non-technical skills. In round two, the Copenhagen Academy for Medical Education and Simulation Needs Assessment Formula (CAMES-NAF) was used to preliminarily prioritize the technical skills according to frequency, importance of competency, SBT impact on patient safety, and feasibility for SBT. In round three, the participants further refined and reprioritized the technical skills. Items achieving consensus (agreement of ≥75%) were included.RESULTS: We included 168 participants from 10 European countries. The response rates in rounds two and three were 80% (135/168) and 87% (117/135), respectively. In round one, the participants suggested 1964 different items. Content analysis revealed 81 unique technical skills prioritized in round two. In round three, 39 technical skills achieved consensus and were included.CONCLUSION: We reached a European consensus on a prioritized list of 39 technical skills to be included in a SBT curriculum in neonatology.

Background: Artificial intelligence (AI) is becoming increasingly used in medical education, but our understanding of the validity of AI-based assessments (AIBA) as compared with traditional clinical expert-based assessments (EBA) is limited. In this study, the authors aimed to compare and contrast the validity evidence for the assessment of a complex clinical skill based on scores generated from an AI and trained clinical experts, respectively. Methods: The study was conducted between September 2020 to October 2022. The authors used Kane's validity framework to prioritise and organise their evidence according to the four inferences: scoring, generalisation, extrapolation and implications. The context of the study was chorionic villus sampling performed within the simulated setting. AIBA and EBA were used to evaluate performances of experts, intermediates and novice based on video recordings. The clinical experts used a scoring instrument developed in a previous international consensus study. The AI used convolutional neural networks for capturing features on video recordings, motion tracking and eye movements to arrive at a final composite score. Results: A total of 45 individuals participated in the study (22 novices, 12 intermediates and 11 experts). The authors demonstrated validity evidence for scoring, generalisation, extrapolation and implications for both EBA and AIBA. The plausibility of assumptions related to scoring, evidence of reproducibility and relation to different training levels was examined. Issues relating to construct underrepresentation, lack of explainability, and threats to robustness were identified as potential weak links in the AIBA validity argument compared with the EBA validity argument. Conclusion: There were weak links in the use of AIBA compared with EBA, mainly in their representation of the underlying construct but also regarding their explainability and ability to transfer to other datasets. However, combining AI and clinical e

To collect validity evidence for the assessment of surgical competence through the classification of general surgical gestures for a simulated robot-assisted radical prostatectomy (RARP). We used 165 video recordings of novice and experienced RARP surgeons performing three parts of the RARP procedure on the RobotiX Mentor. We annotated the surgical tasks with different surgical gestures: dissection, hemostatic control, application of clips, needle handling, and suturing. The gestures were analyzed using idle time (periods with minimal instrument movements) and active time (whenever a surgical gesture was annotated). The distribution of surgical gestures was described using a one-dimensional heat map, snail tracks. All surgeons had a similar percentage of idle time but novices had longer phases of idle time (mean time: 21 vs. 15 s, p < 0.001). Novices used a higher total number of surgical gestures (number of phases: 45 vs. 35, p < 0.001) and each phase was longer compared with those of the experienced surgeons (mean time: 10 vs. 8 s, p < 0.001). There was a different pattern of gestures between novices and experienced surgeons as seen by a different distribution of the phases. General surgical gestures can be used to assess surgical competence in simulated RARP and can be displayed as a visual tool to show how performance is improving. The established pass/fail level may be used to ensure the competence of the residents before proceeding with supervised real-life surgery. The next step is to investigate if the developed tool can optimize automated feedback during simulator training.

Background: Training and assessment of operator competence for the less invasive surfactant administration (LISA) procedure vary. This study aimed to obtain international expert consensus on LISA training (LISA curriculum (LISA-CUR)) and assessment (LISA assessment tool (LISA-AT)). Methods: From February to July 2022, an international three-round Delphi process gathered opinions from LISA experts (researchers, curriculum developers, and clinical educators) on a list of items to be included in a LISA-CUR and LISA-AT (Round 1). The experts rated the importance of each item (Round 2). Items supported by more than 80% consensus were included. All experts were asked to approve or reject the final LISA-CUR and LISA-AT (Round 3). Results: A total of 153 experts from 14 countries participated in Round 1, and the response rate for Rounds 2 and 3 was >80%. Round 1 identified 44 items for LISA-CUR and 22 for LISA-AT. Round 2 excluded 15 items for the LISA-CUR and 7 items for the LISA-AT. Round 3 resulted in a strong consensus (99–100%) for the final 29 items for the LISA-CUR and 15 items for the LISA-AT. Conclusions: This Delphi process established an international consensus on a training curriculum and content evidence for the assessment of LISA competence. Impact: This international consensus-based expert statement provides content on a curriculum for the less invasive surfactant administration procedure (LISA-CUR) that may be partnered with existing evidence-based strategies to optimize and standardize LISA training in the future.This international consensus-based expert statement also provides content on an assessment tool for the LISA procedure (LISA-AT) that can help to evaluate competence in LISA operators. The proposed LISA-AT enables standardized, continuous feedback and assessment until achieving proficiency.

Objective: This study aimed to investigate the validity of simulation-based assessment of robotic-assisted cardiac surgery skills using a wet lab model, focusing on the use of a time-based score (TBS) and modified Global Evaluative Assessment of Robotic Skills (mGEARS) score. Methods: We tested 3 wet lab tasks (atrial closure, mitral annular stitches, and internal thoracic artery [ITA] dissection) with both experienced robotic cardiac surgeons and novices from multiple European centers. The tasks were assessed using 2 tools: TBS and mGEARS score. Reliability, internal consistency, and the ability to discriminate between different levels of competence were evaluated. Results: The results demonstrated a high internal consistency for all 3 tasks using mGEARS assessment tool. The mGEARS score and TBS could reliably discriminate between different levels of competence for the atrial closure and mitral stitches tasks but not for the ITA harvesting task. A generalizability study also revealed that it was feasible to assess competency of the atrial closure and mitral stitches tasks using mGEARS but not the ITA dissection task. Pass/fail scores were established for each task using both TBS and mGEARS assessment tools. Conclusions: The study provides sufficient evidence for using TBS and mGEARS scores in evaluating robotic-assisted cardiac surgery skills in wet lab settings for intracardiac tasks. Combining both assessment tools enhances the evaluation of proficiency in robotic cardiac surgery, paving the way for standardized, evidence-based preclinical training and credentialing. Clinical trial registry number: NCT05043064.

ackground Testing is critical for detecting SARS-CoV-2 infection, but the best sampling method remains unclear. Objectives To determine whether nasopharyngeal swab (NPS), oropharyngeal swab (OPS) or saliva specimen collection has the highest detection rate for SARS-CoV-2 molecular testing. Methods We conducted a randomised clinical trial at two COVID-19 outpatient test centres where NPS, OPS and saliva specimens were collected by healthcare workers in different orders for reverse transcriptase PCR testing. The SARS-CoV-2 detection rate was calculated as the number positive by a specific sampling method divided by the number in which any of the three sampling methods was positive. As secondary outcomes, test-related discomfort was measured with an 11-point numeric scale and cost-effectiveness was calculated. Results Among 23 102 adults completing the trial, 381 (1.65%) were SARS-CoV-2 positive. The SARS-CoV-2 detection rate was higher for OPSs, 78.7% (95% CI 74.3 to 82.7), compared with NPSs, 72.7% (95% CI 67.9 to 77.1) (p=0.049) and compared with saliva sampling, 61.9% (95% CI 56.9 to 66.8) (p<0.001). The discomfort score was highest for NPSs, at 5.76 (SD, 2.52), followed by OPSs, at 3.16 (SD 3.16) and saliva samples, at 1.03 (SD 18.8), p<0.001 between all measurements. Saliva specimens were associated with the lowest cost, and the incremental costs per detected SARS-CoV-2 infection for NPSs and OPSs were US$3258 and US$1832, respectively. Conclusions OPSs were associated with higher SARS-CoV-2 detection and lower test-related discomfort than NPSs for SARS-CoV-2 testing. Saliva sampling had the lowest SARS-CoV-2 detection but was the least costly strategy for mass testing., Background Testing is critical for detecting SARS-CoV-2 infection, but the best sampling method remains unclear. Objectives To determine whether nasopharyngeal swab (NPS), oropharyngeal swab (OPS) or saliva specimen collection has the highest detection rate for SARS-CoV-2 molecular testing. Methods We conducted a randomised clinical trial at two COVID-19 outpatient test centres where NPS, OPS and saliva specimens were collected by healthcare workers in different orders for reverse transcriptase PCR testing. The SARS-CoV-2 detection rate was calculated as the number positive by a specific sampling method divided by the number in which any of the three sampling methods was positive. As secondary outcomes, test-related discomfort was measured with an 11-point numeric scale and cost-effectiveness was calculated. Results Among 23 102 adults completing the trial, 381 (1.65%) were SARS-CoV-2 positive. The SARS-CoV-2 detection rate was higher for OPSs, 78.7% (95% CI 74.3 to 82.7), compared with NPSs, 72.7% (95% CI 67.9 to 77.1) (p=0.049) and compared with saliva sampling, 61.9% (95% CI 56.9 to 66.8) (p<0.001). The discomfort score was highest for NPSs, at 5.76 (SD, 2.52), followed by OPSs, at 3.16 (SD 3.16) and saliva samples, at 1.03 (SD 18.8), p<0.001 between all measurements. Saliva specimens were associated with the lowest cost, and the incremental costs per detected SARS-CoV-2 infection for NPSs and OPSs were US$3258 and US$1832, respectively. Conclusions OPSs were associated with higher SARS-CoV-2 detection and lower test-related discomfort than NPSs for SARS-CoV-2 testing. Saliva sampling had the lowest SARS-CoV-2 detection but was the least costly strategy for mass testing. Trial registration number NCT04715607.

The use of Artificial Intelligence (AI) in medical education has the potential to facilitate complicated tasks and improve efficiency. For example, AI could help automate assessment of written responses, or provide feedback on medical image interpretations with excellent reliability. While applications of AI in learning, instruction, and assessment are growing, further exploration is still required. There exist few conceptual or methodological guides for medical educators wishing to evaluate or engage in AI research. In this guide, we aim to: 1) describe practical considerations involved in reading and conducting studies in medical education using AI, 2) define basic terminology and 3) identify which medical education problems and data are ideally-suited for using AI.

Introduction: In image interpretation education, many educators have shifted away from traditional methods that involve passive instruction and fragmented learning to interactive ones that promote active engagement and integrated knowledge. By training pattern recognition skills in an effective manner, these interactive approaches provide a promising direction for dermoscopy education. Objectives: A narrative review of the literature was performed to probe emerging directions in medical image interpretation education that may support dermoscopy education. This article represents the second of a two-part review series. Methods: To promote innovation in dermoscopy education, the International Skin Imaging Collaborative (ISIC) assembled an Education Working Group that comprises international dermoscopy experts and educational scientists. Based on a preliminary literature review and their experiences as educators, the group developed and refined a list of innovative approaches through multiple rounds of discussion and feedback. For each approach, literature searches were performed for relevant articles. Results: Through a consensus-based approach, the group identified a number of theory-based approaches, as discussed in the first part of this series. The group also acknowledged the role of motivation, metacognition, and early failures in optimizing the learning process. Other promising teaching tools included gamification, social media, and perceptual and adaptive learning modules (PALMs). Conclusions: Over the years, many dermoscopy educators may have intuitively adopted these instructional strategies in response to learner feedback, personal observations, and changes in the learning environment. For dermoscopy training, PALMs may be especially valuable in that they provide immediate feedback and adapt the training schedule to the individual’s performance.

Introduction: Efficient interpretation of dermoscopic images relies on pattern recognition, and the development of expert-level proficiency typically requires extensive training and years of practice. While traditional methods of transferring knowledge have proven effective, technological advances may significantly improve upon these strategies and better equip dermoscopy learners with the pattern recognition skills required for real-world practice. Objectives: A narrative review of the literature was performed to explore emerging directions in medical image interpretation education that may enhance dermoscopy education. This article represents the first of a two-part review series on this topic. Methods: To promote innovation in dermoscopy education, the International Skin Imaging Collaborative (ISIC) assembled a 12-member Education Working Group that comprises international dermoscopy experts and educational scientists. Based on a preliminary literature review and their experiences as educators, the group developed and refined a list of innovative approaches through multiple rounds of discussion and feedback. For each approach, literature searches were performed for relevant articles. Results: Through a consensus-based approach, the group identified a number of emerging directions in image interpretation education. The following theory-based approaches will be discussed in this first part: whole-task learning, microlearning, perceptual learning, and adaptive learning. Conclusions: Compared to traditional methods, these theory-based approaches may enhance dermoscopy education by making learning more engaging and interactive and reducing the amount of time required to develop expert-level pattern recognition skills. Further exploration is needed to determine how these approaches can be seamlessly and successfully integrated to optimize dermoscopy education.

Introduction During a health crisis, hospitals must prioritise activities and resources, which can compromise clerkship-based learning. We explored how health crises affect clinical clerkships using the COVID-19 pandemic as an example. Methods In a constructivist qualitative study, we conducted 22 semi-structured interviews with key stakeholders (i.e. medical students and doctors) from two teaching hospitals and 10 different departments. We used thematic analysis to investigate our data and used stakeholder theory as a sensitising concept. Results We identified three themes: (1) emotional triggers and reactions; (2) negotiation of legitimacy; and (3) building resilience. Our results suggest that the health crisis accentuated already existing problems in clerkships, such as students' feelings of low legitimacy, constant negotiation of roles, inconsistencies navigating rules and regulations and low levels of active participation. Medical students and doctors adapted to the new organisational demands by developing increased resilience. Students responded by reaching out for guidance and acceptance to remain relevant in the clinical clerkships. Doctors developed a behaviour of closing in and focused on managing themselves and their patients. This created tension between these two stakeholder groups. Conclusion A health crisis can critically disrupt the hierarchical structure within the clinical clerkships and exacerbate existing conflicts between stakeholder groups. When medical students are not perceived as legitimate stakeholders in clinical clerkships during a health crisis, their attendance is perceived as unnecessary or even a nuisance. Despite increased student proactiveness and resilience, their roles inevitably shift from being doctors-to-be to students-to-be-managed.

Objectives Emergency caesarean sections (ECS) are time-sensitive procedures. Multiple factors may affect team efficiency but their relative importance remains unknown. This study aimed to identify the most important predictors contributing to quality of care during ECS in terms of the arrival-to-delivery interval. Design A retrospective cohort study. ECS were classified by urgency using emergency categories one/two and three (delivery within 30 and 60 min). In total, 92 predictor variables were included in the analysis and grouped as follows: 'Maternal objective', 'Maternal psychological', 'Fetal factors', 'ECS Indication', 'Emergency category', 'Type of anaesthesia', 'Team member qualifications and experience' and 'Procedural'. Data was analysed with a linear regression model using elastic net regularisation and jackknife technique to improve generalisability. The relative influence of the predictors, percentage significant predictor weight (PSPW) was calculated for each predictor to visualise the main determinants of arrival-to-delivery interval. Setting and participants Patient records for mothers undergoing ECS between 2010 and 2017, Nordsjællands Hospital, Capital Region of Denmark. Primary outcome measures Arrival-to-delivery interval during ECS. Results Data was obtained from 2409 patient records for women undergoing ECS. The group of predictors representing 'Team member qualifications and experience' was the most important predictor of arrival-to-delivery interval in all ECS emergency categories (PSPW 25.9% for ECS category one/two; PSPW 35.5% for ECS category three). In ECS category one/two the 'Indication for ECS' was the second most important predictor group (PSPW 24.9%). In ECS category three, the second most important predictor group was 'Maternal objective predictors' (PSPW 24.2%). Conclusion This study provides empirical evidence for the importance of team member qualifications and experience relative to other predictors of arrival-to-delivery duri

Background: Artificial intelligence can be trained to outperform dermatologists in image‐based skin cancer diagnostics. However, the networks' sensitivity to biases and overfitting may hamper their clinical applicability. Objectives: The aim of this study was to explain the potential consequences of implementing convolutional neural networks for stand‐alone melanoma diagnostics and skin lesion triage. Methods: In this algorithm validation study on retrospective data, we reproduced and evaluated the performance of state‐of‐the‐art artificial intelligence (convolutional neural networks) for skin cancer diagnostics. The networks were trained on 25,331 annotated dermoscopic skin lesion images from an open‐source data set (ISIC‐2019) and tested using a novel data set (AISC‐2021) consisting of 26,591 annotated dermoscopic skin lesion images. We tested the trained algorithms' ability to generalize to new data and their diagnostic performance in two simulations (melanoma diagnostics and skin lesion triage). Results: The trained algorithms performed significantly less accurate diagnostics on images of nevi, melanomas and actinic keratoses from the AISC‐2021 data set than the ISIC‐2019 data set (p < 0.003). Almost one‐third (31.1%) of the melanomas were misclassified during the melanoma diagnostics simulation, irrespective of their Breslow thickness. Furthermore, the algorithms marked 92.7% of the lesions ‘suspicious’ during the triage simulation, which yielded a triage sensitivity and specificity of 99.7% and 8.2%, respectively. Conclusions: Although state‐of‐the‐art artificial intelligence outperforms dermatologists on image‐based skin lesion classification within an artificial setting, additional data and technological advances are needed before clinical implementation.

The purpose of this scoping review was to explore how errors are conceptualized in medical education contexts by examining different error perspectives and practices. This review used a scoping methodology with a systematic search strategy to identify relevant studies, written in English, and published before January 2021. Four medical education journals (Medical Education, Advances in Health Science Education, Medical Teacher, and Academic Medicine) and four clinical journals (Journal of the American Medical Association, Journal of General Internal Medicine, Annals of Surgery, and British Medical Journal) were purposively selected. Data extraction was charted according to a data collection form. Of 1505 screened studies, 79 studies were included. Three overarching perspectives were identified: ‘understanding errors’) (n = 31), ‘avoiding errors’ (n = 25), ‘learning from errors’ (n = 23). Studies that aimed at’understanding errors’ used qualitative methods (19/31, 61.3%) and took place in the clinical setting (19/31, 61.3%), whereas studies that aimed at ‘avoiding errors’ and ‘learning from errors’ used quantitative methods (‘avoiding errors’: 20/25, 80%, and ‘learning from errors’: 16/23, 69.6%, p = 0.007) and took place in pre-clinical (14/25, 56%) and simulated settings (10/23, 43.5%), respectively (p < 0.001). The three perspectives differed significantly in terms of inclusion of educational theory: ‘Understanding errors’ studies 16.1% (5/31),’avoiding errors’ studies 48% (12/25), and ‘learning from errors’ studies 73.9% (17/23), p < 0.001. Errors in medical education and clinical practice are defined differently, which makes comparisons difficult. A uniform understanding is not necessarily a goal but improving transparency and clarity of how errors are currently conceptualized may improve our understanding of when, why, and how to use and learn from errors in the future.

Introduction: Efficient interpretation of dermoscopic images relies on pattern recognition, and the development of expert-level proficiency typically requires extensive training and years of practice. While traditional methods of transferring knowledge have proven effective, technological advances may significantly improve upon these strategies and better equip dermoscopy learners with the pattern recognition skills required for real-world practice. Objectives: A narrative review of the literature was performed to explore emerging directions in medical image interpretation education that may enhance dermoscopy education. This article represents the first of a two-part review series on this topic. Methods: To promote innovation in dermoscopy education, the International Skin Imaging Collaboration (ISIC)assembled a 12-member Education Working Group that comprises international dermoscopy experts and educational scientists. Based on a preliminary literature review and their experiences as educators, the group developed and refined a list of innovative approaches through multiple rounds of discussion and feedback. For each approach, literature searches were performed for relevant articles. Results: Through a consensus-based approach, the group identified a number of emerging directions in image interpretation education. The following theory-based approaches will be discussed in this first part: whole-task learning, microlearning, perceptual learning, and adaptive learning. Conclusions: Compared to traditional methods, these theory-based approaches may enhance dermoscopy education by making learning more engaging and interactive and reducing the amount of time required to develop expert-level pattern recognition skills. Further exploration is needed to determine how these approaches can be seamlessly and successfully integrated to optimize dermoscopy education.

Introduction: In image interpretation education, many educators have shifted away from traditional methods that involve passive instruction and fragmented learning to interactive ones that promote active engagement and integrated knowledge. By training pattern recognition skills in an effective manner, these interactive approaches provide a promising direction for dermoscopy education. Objectives: A narrative review of the literature was performed to probe emerging directions in medical image interpretation education that may support dermoscopy education. This article represents the second of a two-part review series. Methods: To promote innovation in dermoscopy education, the International Skin Imaging Collaboration (ISIC) assembled an Education Working Group that comprises international dermoscopy experts and educational scientists. Based on a preliminary literature review and their experiences as educators, the group developed and refined a list of innovative approaches through multiple rounds of discussion and feedback. For each approach, literature searches were performed for relevant articles. Results: Through a consensus-based approach, the group identified a number of theory-based approaches, as discussed in the first part of this series. The group also acknowledged the role of motivation, metacognition, and early failures in optimizing the learning process. Other promising teaching tools included gamification, social media, and perceptual and adaptive learning modules (PALMs). Conclusions: Over the years, many dermoscopy educators may have intuitively adopted these instructional strategies in response to learner feedback, personal observations, and changes in the learning environment. For dermoscopy training, PALMs may be especially valuable in that they provide immediate feedback and adapt the training schedule to the individual’s performance.