Level Of Expertise Research Articles

Comparison of Supervised Learning and Unsupervised Learning in the Diagnosis of Medical Imaging Diseases

Medical image registration represents a pivotal element in the field of disease image analysis, acting as the essential precursor for a multitude of sophisticated analytical tasks. In recent years, traditional methodologies have encountered significant challenges in meeting the evolving demands of clinical practice. In contrast, deep learning-based strategies have emerged as powerful alternatives, showcasing their ability to facilitate more rapid and accurate registration processes, thereby exerting a profound impact on clinical applications. Within the specialized domain of medical imaging, the intricate level of expertise required for domain knowledge imposes rigorous standards on annotators, which in turn leads to increased annotation costs. As a result, the efficacy of supervised learning approaches compared to unsupervised learning methodologies can exhibit substantial variability in real-world applications. This paper systematically utilizes a diverse array of medical imaging datasets to rigorously assess the performance outcomes of both supervised and unsupervised learning techniques, specifically in relation to their practical applications in the medical imaging landscape.

From Carrel to Robotics: Renal Transplantation and the Evolution of its Surgical Technique.

Renal transplantation has become the standard of care for patients with end-stage renal disease (ESRD) due to significant advancements across various medical and surgical fields. This review highlights the evolution of renal transplantation techniques, while also exploring the latest tools and practices that promise to further improve their outcomes. Since the first successful renal transplant, numerous innovations have been implemented in the various stages of renal graft implantation. These include improved techniques in vascular and urinary anastomoses, graft positioning, and the recent emergence of modern technologies such as robotics and artificial intelligence (AI) which have shown promise in enhancing surgical performance. The field of renal transplantation is continuously evolving. As new and more efficient technologies continue to emerge, these too will become the standard of care and training. It is essential for transplant surgeons to understand the key historical developments that have led to our current level of expertise, so we can make effective use of these tools in further advancing the field.

Abstract 3351: From bench to bedside: generalizable AI model for ADC biomarker evaluation in NSCLC

Abstract Background: Antibody-drug conjugates (ADCs) targeting trophoblast cell-surface antigen 2 (TROP-2) and hepatocyte growth factor receptor (cMET) are promising therapies for non-small cell lung cancer (NSCLC). However, their clinical application requires robust and rapid biomarker evaluation that addresses expression heterogeneity and avoid interobserver variability. Current approaches based on pathologist assessments are limited by subjectivity and scalability. This study aimed to develop a generalizable AI model for ADC biomarker evaluation, trained on TROP-2 and inferred on cMET, to validate its adaptability across markers. Additionally, the model’s performance was compared with expert pathologists to assess its clinical utility. Finally, biomarker prevalence in the two main NSCLC subtypes, namely adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC), was evaluated. Methods: We collected a bicentric real-world sample group of 1142 patients with resected NSCLC from the Charité, Berlin and the University Hospital Cologne. For tissue microarray construction, two 1.5-mm tissue cores were punched from formalin-fixed and paraffin-embedded tumor blocks. Immunohistochemical staining for TROP-2 and cMET was performed and sections were scanned for AI-based analysis. The AI pipeline comprised three models: a cell detection model for the identification of cells, a cell classification model for the differentiation of tumor and other cells, and an expression scoring model for membranous biomarker quantification. The model was trained on TROP-2, and subsequently inferred on cMET, thus enabling an evaluation of cross-marker generalization. Five pathologists with varying levels of expertise manually evaluated a representative subset, using H-scoring. Finally, the results were compared with those yielded by the AI model. Results: The expression scoring model achieved a macro-averaged F1 score of 94% for TROP-2 and 91% for cMET. Moreover, the model demonstrated excellent concordance with expert pathologists (TROP-2: 93%; cMET: 95% average pair-wise Pearson correlation). TROP-2 overexpression was significantly higher in LUSC (mean H-score: 154.67) than LUAD (mean H-score: 86.57), while cMET showed the opposite trend (mean H-score LUAD: 59.52; LUSC: 25.68). Conclusion: This study highlights the potential of AI models to address key challenges in ADC biomarker evaluation, including expression heterogeneity, interobserver variability, and reduction in time expenditure. By successfully generalizing between TROP-2 and cMET, the model demonstrates adaptability and scalability for broader clinical applications. These findings pave the way for integrating AI into clinical workflows, improving patient stratification, and optimizing ADC therapy selection. Future efforts will focus on expanding this approach to additional biomarkers and validating its utility in prospective clinical trials. Citation Format: Philipp Anders, Philipp Erwin Seegerer, Katja Lingelbach, Suhas Pandhe, Sandip Ghosh, Cornelius Böhm, Stephan Tietz, Rosemarie Krupar, Lars Tharun, Marie-Lisa Eich, Julika Ribbat-Idel, Verena Aumiller, Sabine Merkelbach-Bruse, Alexander Quaas, Nikolaj Frost, Georg Schlachtenberger, Matthias Heldwein, Ulrich Keilholz, Khosro Hekmat, Jens-Carsten Rückert, Reinhard Büttner, David Horst, Maximilian Alber, Lukas Ruff, Frederick Klauschen, Gabriel Dernbach, Simon Schallenberg. From bench to bedside: generalizable AI model for ADC biomarker evaluation in NSCLC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3351.

Cosmetic Surgery or Not? A Dual Perspective from Professionals and Laypeople on the Facial Aesthetic Improvement of Patients Undergoing Complex Treatments—A Pilot Study

Introduction: Facial aesthetics is an intricate domain bridging biology, psychology, and culture. It is influenced by evolutionary traits and societal norms, often driving demand for cosmetic treatments. While public perceptions of these interventions are widely studied, professional evaluations remain underexplored. This study examines differences in aesthetic judgement and treatment identification between healthcare professionals and laypeople, aiming to enhance clinical practice and research in facial aesthetics. Methods: A cross-sectional survey, administered via Microsoft Forms, assessed aesthetic evaluations from 88 participants, including professionals (e.g., dentists, dermatologists, and plastic surgeons) and laypeople. The survey comprised demographic questions, self-reported aesthetic assessment abilities, and visual evaluations of pre- and post-treatment images. Statistical analyses, including Fisher’s exact test and the Mann–Whitney U Test, evaluated associations between expertise level and judgments of facial beauty and treatment accuracy. Results: This study found no significant differences in subjective aesthetic improvement ratings between professionals and laypeople, suggesting that aesthetic judgments are inherently subjective. However, professionals demonstrated greater accuracy in identifying treatments in 3 of 7 cases (p < 0.05). Self-assessment revealed a critical disparity, with experts often underrating their skills compared to non-experts. Discussion: These findings highlight the interplay between expertise and subjectivity in aesthetic evaluations. While professional training enhances procedural recognition, biases influence judgement regardless of expertise. Incorporating standardised evaluation frameworks can refine aesthetic assessments across groups. Conclusions: This study underscores the value of expertise in treatment identification and advocates for standardised assessment methods to minimise bias, improve training, and support patient-centred aesthetic care.

Automated assessment of simulated laparoscopic surgical skill performance using deep learning

Artificial intelligence (AI) has the potential to improve healthcare and patient safety and is currently being adopted across various fields of medicine and healthcare. AI and in particular computer vision (CV) are well suited to the analysis of minimally invasive surgical simulation videos for training and performance improvement. CV techniques have rapidly improved in recent years from accurately recognizing objects, instruments, and gestures to phases of surgery and more recently to remembering past surgical steps. Lack of labeled data is a particular problem in surgery considering its complexity, as human annotation and manual assessment are both expensive in time and cost, and in most cases rely on direct intervention of clinical expertise. In this study, we introduce a newly collected simulated Laparoscopic Surgical Performance Dataset (LSPD) specifically designed to address these challenges. Unlike existing datasets that focus on instrument tracking or anatomical structure recognition, the LSPD is tailored for evaluating simulated laparoscopic surgical skill performance at various expertise levels. We provide detailed statistical analyses to identify and compare poorly performed and well-executed operations across different skill levels (novice, trainee, expert) for three specific skills: stack, bands, and tower. We employ a 3-dimensional convolutional neural network (3DCNN) with a weakly-supervised approach to classify the experience levels of surgeons. Our results show that the 3DCNN effectively distinguishes between novices, trainees, and experts, achieving an F1 score of 0.91 and an AUC of 0.92. This study highlights the value of the LSPD dataset and demonstrates the potential of leveraging 3DCNN-based and weakly-supervised approaches to automate the evaluation of surgical performance, reducing reliance on manual expert annotation and assessments. These advancements contribute to improving surgical training and performance analysis.

First Year in a New Leadership Role: Lessons Learned.

First Year in a New Leadership Role: Lessons Learned.

Study on Ultrasound-Assisted Diagnosis of CHB Complicated with NAFLD Hepatic Fibrosis Based on Deep Learning.

This study aimed to develop and evaluate an automated classification model for diagnosing chronic hepatitis B (CHB) complicated with non-alcoholic fatty liver disease (NAFLD) using deep learning techniques applied to two-dimensional liver imaging. We retrospectively analyzed data from 2803 patients diagnosed with CHB and NAFLD via two-dimensional ultrasound and FibroScan at our hospital between June 2019 and December 2022. These patients contributed a total of 20,540 two-dimensional liver images. An additional 150 patients with CHB complicated with NAFLD, who had liver biopsy results, were selected (with a total of 922 liver 2D ultrasound images) for validation of the deep learning model. The diagnostic performance was assessed using sensitivity, specificity, and accuracy metrics for independent diagnoses made by clinicians or with AI assistance. Diagnostic performance was assessed under blinded conditions by physicians of varying expertise, primary, intermediate, and senior clinicians, either independently or assisted by the AI model. We then conducted a statistical analysis of the results, focusing on sensitivity, specificity, and accuracy metrics. The sensitivity, specificity, and accuracy for independent diagnoses made by primary, intermediate, and senior physicians were 24.3%, 29.1%, and 33.8% for sensitivity; 100% for specificity; and 25.3%, 30.0%, and 34.7% for accuracy, respectively. The AI model alone achieved 68.9% sensitivity, 100% specificity, and 69.3% accuracy. When diagnoses were AI-assisted, the sensitivity increased significantly to 73.7% for primary physicians, 73.7% for intermediate physicians, and 75.7% for senior physicians, while specificity remained at 100%, and accuracy was 74.0%, 74.0%, and 76.0%, respectively. The area under the receiver operating characteristic curve (AUC) for AI-independent diagnosis (0.845) was significantly higher than that for primary, intermediate, and senior physicians (0.622, 0.645, and 0.669, respectively; ). Similarly, AI-assisted diagnosis AUC values (0.868, 0.868, and 0.878, respectively) surpassed those for independent diagnosis by physicians at each level of expertise ( ). In conclusion, the deep learning model based on two-dimensional liver imaging demonstrated feasibility for assisting in the identification of liver fibrosis in patients with CHB and NAFLD. Our results suggest that integrating AI models into the diagnostic process can significantly improve the accuracy of diagnosing liver conditions in this patient population.

Assessment of the thorax is equivalent when presented with whole body versus collimated thoracic radiographs in dogs and cats

BackgroundAnalog radiographic techniques traditionally focus on collimation to enhance diagnostic quality by limiting the field to the area of interest. With digital radiology systems, this study hypothesized that whole-body radiographs are diagnostically equivalent to collimated thoracic radiographs. Conducted at a university teaching hospital from November 2019 to April 2021, the study involved 40 client-owned small-breed dogs and cats presenting with respiratory or cardiovascular complaints. Each animal underwent both whole-body radiographs and collimated thoracic radiographs, and the radiographs were evaluated by three board-certified radiologists and three general practitioners in a blinded, randomized manner, based on a 12-question Likert scale survey.ResultsDiagnostic assessments of thoracic findings were equivalent between whole-body radiographs and collimated thoracic radiographs, with no significant differences identified by the six reviewers, irrespective of their level of expertise. These findings confirm that whole-body radiographs and collimated thoracic radiographs are diagnostically comparable for identifying thoracic abnormal findings, including cardiomegaly (n = 19), bronchial pattern (n = 12), esophageal dilation (n = 7), alveolar pattern (presumably pneumonia) (n = 6), narrowing of the trachea and bronchi (n = 5), left-sided congestive heart failure (n = 4), pleural effusion (n = 4), rib fractures (n = 2), pulmonary mass/nodule (n = 2), transitional vertebrae (n = 2), intrathoracic lymphadenopathy (n = 1), and hypovolemia (n = 1).ConclusionsThe study supports whole-body radiographs as a practical alternative to collimated thoracic radiographs for evaluating thoracic abnormal findings in small animals. WBR offers a simpler imaging approach without compromising diagnostic accuracy, providing a flexible and reliable option in clinical practice.

Assessing the Implementation of Competency-Based Education and Training in Kenya: A Case Study of the Nyeri National Polytechnic, Kenya

Competency-based education and training (CBET) focuses on developing practical skills, knowledge, and attributes in trainees rather than just theoretical knowledge. Launched by the Ministry of Education in Kenya in 2018, CBET aims to prepare trainees for real-world employment, making them professionals. However, traditional knowledge-based technical training methods have not been fully phased out in technical training institutions. This study aimed to assess the implementation of CBET in Kenya using The Nyeri National Polytechnic (NNP) in Nyeri, Kenya, as the case study. The research focused on the following objectives: to determine the level of pedagogical knowledge of the CBET approach among CBET trainers at NNP, to explore instructional processes CBET trainers use in content delivery at NNP, and to find out the assessment methods CBET trainers use to evaluate the competence levels developed by CBET trainees at NNP. Vygotsky's constructivist theory of learning guided this study, emphasising that learners actively construct knowledge. A descriptive case study design was used, targeting a population of 3,887, including 265 CBET trainers and 3,622 trainees at NNP. The sample comprised 363 respondents: 25 trainers and 338 trainees, selected through purposive and stratified purposeful sampling. Data were collected via questionnaires, coded in SPSS, and analysed using descriptive statistics like frequency, percentage, and mean, presented through narratives and tables. The study found that CBET trainers demonstrated, on average, an Advanced Level (74%) of pedagogical knowledge in the CBET approach, indicating a solid understanding but with 26% room for improvement to reach Expert Level (90% - 100%). Trainers utilised Universal Design for Learning (UDL), active learning, learner-centred methods, practical sessions, and dual training at an average of 68.8%, while traditional methods comprised 31.2%. Reliance on traditional written assessments was 38.9%, with evaluations occurring only once during trainees' four-month industry attachments, supplemented by written assessments (CATs and end-of-term exams) to assess competence. Recommendations highlight opportunities for professional development to elevate trainers to an Expert Level. Key interventions include refining instructional methods, enhancing resource availability, and strengthening partnerships for dual training. It is also vital for CBET trainers to adopt innovative assessment tools to effectively evaluate trainees’ competencies in line with industry standards. This research offers valuable insights for education stakeholders, providing a basis for assessing CBET implementation in technical institutions. It aids policymakers in refining strategies to enhance CBET initiatives and contributes to the knowledge base for future research in this area.

Development of an Interactive Online Course on Artificial Intelligence in Medicine: A Study on Design and Implementation.

As artificial intelligence (AI) becomes increasingly influential in healthcare, there is a pressing need for medical professionals to gain a comprehensive understanding of its applications, particularly in complex areas such as rare diseases, where diagnostic challenges are significant. To address this knowledge gap, we are developing an interactive online course, Artificial Intelligence for Rare Diseases, hosted on "KI Campus", a German learning platform for AI. The course combines interactive videos, case-based learning, and self-assessment tools to engage medical professionals, data scientists, and students. This paper outlines the course's design and the challenges of making complex AI concepts accessible. The modular format and multimodal tools ensure effective learning across varied expertise levels.

Advanced and long-term meditation and the autonomic nervous system: A review and synthesis.

Advanced and long-term meditation and the autonomic nervous system: A review and synthesis.

Insight elements of mathematical problem solving in generally gifted and mathematical experts: ERP amplitudes in PO electrodes.

School mathematics mainly embraces algorithmic problem solving, pays less attention to strategic reasoning, and rarely contains insightful problem solving. Based on our previous research, we hypothesize that success in solving insight problems correlates strongly with general giftedness, while mathematical expertise is essential for strategy-based problem solving. Furthermore, we employ a phenomenon of greater ERP amplitudes in PO4/8 electrodes associated with insightful problem solving. In this study, 114 high school students (aged 16-18) with varying degrees of general giftedness and mathematical expertise were asked to solve mathematical problem of three distinct type: (1) function problems, whose solutions are memory-based; (2) area problems that necessitate strategic thinking; and (3) insight problems, that necessitate insight for their resolution. The problem solving process was accompanied by ERP recording. We demonstrate that variations in accuracy of solutions and reaction time for correct responses between tasks are influenced by students' general giftedness and mathematical expertise. Our ERP analyses partly supported our hypotheses regarding the relationship between PO electrode activation, insight-based problem solving processes, and participants' levels of giftedness and mathematical expertise.

Tailoring Scientific Knowledge: How Generative AI Personalizes Academic Reading Experiences

The scientific literature is expanding at an unprecedented pace, making it increasingly difficult for researchers, students, and professionals to extract relevant insights efficiently. Traditional academic publishing offers static, one-size-fits-all content that does not cater to the diverse backgrounds, expertise levels, and interests of readers. This paper explores how generative AI can dynamically personalize scholarly content by tailoring summaries and key takeaways to individual user profiles. Nine scientific articles from a single journal issue were used to create the dataset, and prompt engineering was applied to generate tailored insights for exemplary personas: a digital humanities and open science researcher, and a mining and raw materials industry specialist. The effectiveness of AI-generated content modifications in enhancing readability, comprehension, and relevance was evaluated. The results indicate that generative AI can successfully emphasize different aspects of an article, making it more accessible and engaging to specific audiences. However, challenges such as content oversimplification, potential biases, and ethical considerations remain. The implications of AI-powered personalization in scholarly communication are discussed, and future research directions are proposed to refine and optimize AI-driven adaptive reading experiences.

Enhancing conceptual teaching in organic chemistry through lesson study: a TSPCK-Based approach

Abstract Conceptual teaching in science education and its implications for pedagogical research have been extensively studied. Likewise, lesson study (LS) has been recognized as a powerful tool for continuous professional development and effective teaching practices. Despite the recognized importance of LS and the affordances of topic-specific PCK (TSPCK) in developing conceptual teaching, the connection between these two approaches in fostering conceptual teaching, remains under-explored within science education. This study aims to explore the development of a conceptual teaching from the dynamic interplay of TSPCK components for teaching a section of organic reactions through a TSPCK-based LS. A qualitative research approach was followed with four teachers from Umkhanyakude district in KwaZulu-Natal. These teachers participated in a TSPCK-based LS to develop a conceptual teaching strategy using Content Representation (CoRe). The findings revealed that teachers progressed from a basic to a developing level of conceptual teaching expertise through LS, as measured by TSPCK component interaction. While the findings are not generalizable, they offer valuable insights into teacher development, highlighting LS as an effective approach for enhancing conceptual teaching in science. This study concludes by recommending large-scale research exploring the role of reflection and feedback during LS in developing conceptual teaching within TSPCK interventions.

Stacking Model-Based Classifiers for Dealing With Multiple Sets of Noisy Labels.

Supervised learning in presence of multiple sets of noisy labels is a challenging task that is receiving increasing interest in the ever-evolving landscape of healthcare analytics. Such an issue arises when multiple annotators are tasked to manually label the same training samples, potentially giving rise to discrepancies in class assignments among the supplied labels with respect to the ground truth. Commonly, the labeling process is entrusted to a small group of domain experts, and different level of experience and subjectivity may result in noisy training labels. To solve the classification task leveraging on the availability of multiple data annotators, we introduce a novel ensemble methodology constructed combining model-based classifiers separately trained on single sets of noisy labels. Eigenvalue Decomposition Discriminant Analysis is employed for the definition of the base learners, and six distinct averaging strategies are proposed to combine them. Two solutions necessitate a priori information, such as the partial knowledge of the ground truth labels or the annotators' level of expertise. Differently, the remaining four approaches are entirely data-driven. A simulation study and an application on real data showcase the improved predictive performance of our proposal, while also demonstrating the ability of automatically inferring annotators' expertise level as a by-product of the learning process.

Reliability of rectal MRI radiomic features: Comparing rectal MRI radiomic features across reader expertise levels, image segmentation technique, and timing of rectal MRI in patients with locally advanced rectal cancer.

Reliability of rectal MRI radiomic features: Comparing rectal MRI radiomic features across reader expertise levels, image segmentation technique, and timing of rectal MRI in patients with locally advanced rectal cancer.

Influence of musical background on children's handwriting: Effects of melody and rhythm.

Influence of musical background on children's handwriting: Effects of melody and rhythm.

The Wellbeing of the Haematology Workforce in the UK.

Globally, haematology is recognised as a highly specialised field of practice that deals with the diagnosis, treatment, and management of blood disorders. To meet the demand of increased service delivery, the workforce needs to be able to adapt and respond to challenges. Specialists and organisations require specific intelligence to understand their workforce, the demand for labour, and plan for the future. This study aimed to understand wellbeing among the haematology workforce across the multidisciplinary team. A mixed methods explanatory sequential design was used to collect data on wellbeing. Stage 1 online questionnaire was distributed through membership networks. Descriptive statistics were used to analyse the data. Stage 2, data were collected through online semi-structured interviews and analysed thematically. Ethical approval was gained through University Ethics Panel. Haematology professionals face multiple stressors due to exposure of emotive situations. This study found high levels of burnout and frustration, with professionals saying that, they were exhausted after each shift. The increasing demands of the haematology service contribute to the overall pressure experienced by staff, making them feel overwhelmed. Inadequate staffing was a defining challenge in providing adequate service delivery, meeting patients' needs whilst attempting to maintain wellbeing. Working hours /schedule and on-call for many participants reflected their efforts to meet the changing demands within services. This is the first study to focus exclusively on a range of healthcare professionals within haematology. The diverse and demanding nature of work, as well as the complexities of managing haematology patients, emphasised the need for a high level of expertise, adaptability, and resilience within the workforce. Supportive work environments are needed to allow professionals to establish and uphold personal boundaries and disengage with work to ensure a sustainable work-life balance.

Safety of in-hospital delay of appendectomy - a propensity score-matched analysis of 4900 consecutive patients undergoing surgery for suspected appendicitis.

Historically, urgent surgery was advocated in patients with suspected appendicitis because of the risk of perforation and possible complications. Although recent studies have shown that it is safe to delay surgery under certain circumstances, many studies do not report adjusted data and exclude patients based on risk factors. Furthermore, it is unclear whether an ultrasound-based diagnostic workup is sufficient to safely delay surgery. This large retrospective study aimed to analyze the risk-adjusted association between delayed appendectomy and perforation and complication rates. Data from consecutive patients who underwent appendectomy for suspected appendicitis at a single institution were reviewed and analyzed. The investigated outcomes were perforation and complication rates. Propensity score (PS) matching was used to create equal groups regarding confounding factors, and multivariate analysis was performed to control for risk factors and to calculate adjusted odds ratios (ORs) for in-hospital delay. Between January 2008 and June 2023, 4900 patients underwent appendectomy for suspected appendicitis. Ultrasound imaging was performed in 4754 patients. Multivariate analysis of PS-matched data showed no association between a waiting time of >12h and perforation rate (OR, 0.93; 95% CI, 0.67-1.31; P =.69) or complication rate (OR, 0.90; 95% CI, 0.62-1.30; P =.56). Similar results were obtained for a waiting time of 18h and perforation rate (OR, 0.96; 95% CI, 0.48-1.56; P =.88) or complication rate (adjusted OR, 0.97; 95% CI, 0.57-1.68; P =.93). This large PS-matched analysis showed that it is safe to delay surgery by 12 and 18h, even when the diagnostic workup is based on ultrasound. In patients with risk factors for complications, postponement of the procedure can be considered if it can improve overall conditions or allow the procedure to be performed with a higher level of expertise.

26 Clinical Competencies for the Burn Speech-Language Pathologist: A Multidisciplinary Development and Consensus Study

Abstract Introduction Clinical competency guidelines provide a benchmark to promote the provision of standard care to ensure safe and optimal patient management. Whilst nationally agreed clinical competencies are available for other specialized areas of Speech Language Pathology practice, none currently exist for burn injury. Therefore, we aimed to develop a competency tool specific to the burns Speech Language Pathologist (SLP). Methods Spearheaded by the American Burn Association (ABA) Rehabilitation Committee, a group of recognised burn SLPs, Burn Certified Physical and Occupational Therapists and a Burn Physiatrist used a staged approach to (1) synthesize current practice guidelines from a number of national and international burn centers, (2) employ modified Delphi methodology, and subsequently (3) facilitate and participate in expert consensus meetings, to develop and refine a burn SLP competency tool. The previously published Burn Rehabilitation Therapists Competency Tool (Version 2), endorsed by the ABA, was used as a framework. Results Eighteen expert multidisciplinary burn clinicians across 14 medical centers, representing three countries (USA: eight states, Canada: three provinces, Australia: one state) contributed to the development and refinement of the Burns SLP competency tool. A steering committee of five SLPs and the burn physiatrist identified 103 competency statements across 15 core clinical domains relevant to the Burns SLP. These were presented in the Delphi Round 1; 18 participants voted and following a consensus meeting, the tool was refined with statements and domains revised, merged and/or new items created as indicated. Two further Delphi Rounds followed by consensus meetings are currently in process. The final tool will comprise multiple domains (each containing knowledge and application competency statements) tailored specifically to the SLP skill set within adult and pediatric burn injury populations, across the continuum of care from acute through to rehabilitation. The tool presents two tiered levels of expertise: Level 1 = minimum level of specialist skill required to manage a severe burn patient, Level 2 = expert level of specialist skill and recognized resource to other SLPs). Conclusions This competency tool is the first internationally accepted standard of care for burn SLPs and provides a guideline for performance in managing burn patients throughout the acute and rehabilitative care continuum. Applicability of Research to Practice Internationally agreed competencies are needed for the Burns SLP. Development of a clinical competency package through expert multidisciplinary consultation and consensus will guide training, ensure consistency of care provision and benchmarking across facilities with the mutual goal to optimize patient care and outcomes. Once completed, the competencies may serve as a key benchmark to establishing a burn therapy certification for SLPs. Funding for the Study N/A