UNASSIGNED: The use of robotic systems in the surgical domain has become groundbreaking for patients and surgeons in the last decades. While the annual number of robotic surgical procedures continues to increase rapidly, it is essential to provide the surgeon with innovative training courses along with the standard specialization path. To this end, simulators play a fundamental role. Currently, the high cost of the leading VR simulators limits their accessibility to educational institutions. The challenge lies in balancing high-fidelity simulation with cost-effectiveness; however, few cost-effective options exist for robotic surgery training.
UNASSIGNED: This paper proposes the design, development and user-centered usability study of an affordable user interface to control a surgical robot simulator. It consists of a cart equipped with two haptic interfaces, a VR visor and two pedals. The simulations were created using Unity, which offers versatility for expanding the simulator to more complex scenes. An intuitive teleoperation control of the simulated robotic instruments is achieved through a high-level control strategy.
UNASSIGNED: Its affordability and resemblance to real surgeon consoles make it ideal for implementing robotic surgery training programs in medical schools, enhancing accessibility to a broader audience. This is demonstrated by the results of an usability study involving expert surgeons who use surgical robots regularly, expert surgeons without robotic surgery experience, and a control group. The results of the study, which was based on a traditional Peg-board exercise and Camera Control task, demonstrate the simulator\'s high usability and intuitive control across diverse user groups, including those with limited experience. This offers evidence that this affordable system is a promising solution for expanding robotic surgery training.

BACKGROUND: Medical Foundation Year (FY) doctors demonstrate greater psychological distress compared with the general population and other student groups. This feasibility study investigated FY doctors\' perceptions of mindfulness and the impact of a mindful resilience and effectiveness training (MRET) programme on stress, wellbeing, and performance.
METHODS: Mixed-methods study utilising a questionnaire (study 1, N = 144) and a pre-post analysis design of MRET programme (study 2, N = 13), along with focus groups (N = 7).
RESULTS: In study 1 28.5% of FY\'s reported using mindfulness. All five mindfulness facets were significantly, and positively, associated with mental wellbeing (p < 0.05). Acting with awareness (AA) and non-reactivity (NR) were significantly, positively associated with a challenge responses to stress (p < 0.05). Threat and loss appraisals were negatively associated with AA, NR, and non-judging (p < 0.01). Perceived productivity was positively associated with mindfulness facets: describing, AA, and NR (p < 0.001). In study 2, there were significant increases in wellbeing and mindfulness facets observing, describing, AA, and NR, and threat appraisals decreased (p < 0.05). The main themes identified across the focus group included Reframed Mindset, Values-Based Action, Embodied Leadership and Pedagogy.
CONCLUSIONS: There exists a relationship between mindfulness, psychological wellbeing, and performance in FYs. The MRET prorgamme improved psychological wellbeing and reduced threat appraisals. Future work could focus resources on enhancing the skills of AA and NR, as this may be sufficient to bring about meaningful improvements in wellbeing, percieved productivity and cognitive reappraisal of stressful life events.

Background The use of telemedicine in contemporary healthcare has become essential, providing a novel method of delivering care, particularly in rural and underdeveloped areas. This study assesses the telemedicine awareness, knowledge, attitude, skills, and challenges among physicians working in tertiary care hospitals. Methods A cross-sectional study was carried out with 100 doctors from diverse specialties at a tertiary care institution. The questionnaire evaluated five domains: telemedicine awareness and knowledge, telemedicine attitude, telemedicine technology skills, telemedicine utilization patterns, and perceived barriers and educational needs. Results The study indicated that 95% of participants were aware of telemedicine. However, knowledge gaps remained, particularly in legal and ethical concerns (50%) and international rules (40%). Eighty percent of respondents had a favorable opinion of telemedicine, and 85% thought it might improve patient care in rural regions. The proficiency level of telemedicine users was variable: 60% of them had previous experience, and 70% of them rated their proficiency as intermediate or better. According to utilization patterns, 50% of telemedicine users used it at least once a week, primarily for remote monitoring (30%) and video consultations (60%). The study found that 90% of respondents had a high demand for training and educational opportunities. The absence of infrastructure (65%), worries about data security (55%), and patient acceptability (30%) were the main obstacles found. It also highlighted how important it is to have defined policies and collaborate across disciplines. Conclusion The study identifies a good attitude toward telemedicine among doctors as well as a need for improved training and infrastructure. It is essential to tackle these requirements and obstacles in order to successfully incorporate telemedicine into healthcare systems.

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UNASSIGNED: Against the backdrop of poor discharge communication in hospitals, this study explores the purpose of discharge interviews from the physicians\' perspective and the challenges they are confronted with. Discharge interviews are legally required in Germany as part of the discharge management. Led by the ward physician, the discharge interview should summarize relevant information about the hospital stay, medication, lifestyle interventions and follow-up treatment.
UNASSIGNED: Semi-structured interviews with n = 12 physicians were conducted at Heidelberg University Hospital between February and April 2020. Qualitative content analysis was carried out using MAXQDA.
UNASSIGNED: Physicians reported gaining information, providing information, and answering open-ended questions as the purpose of the discharge interview. Challenges in conducting discharge interviews were related to finding a common language, patient-related challenges, conditions of everyday ward life, and lack of training. Physicians reported receiving no explicit training on discharge interviews. While professional experience seems to mitigate the lack of training, some physicians expressed a prevailing sense of insecurity.
UNASSIGNED: The lack of preparation for discharge interviews in medical school makes it particularly challenging for physicians to translate their theoretical knowledge into patient-centered discharge communication. Medical training on discharge interviews should be expanded in terms of theoretical input on the ideal content, its purpose and potential (e.g. in reducing readmissions), as well as practical exercises.

BACKGROUND: Patients with rare and complex diseases often experience delayed diagnoses and misdiagnoses because comprehensive knowledge about these diseases is limited to only a few medical experts. In this context, large language models (LLMs) have emerged as powerful knowledge aggregation tools with applications in clinical decision support and education domains.
OBJECTIVE: This study aims to explore the potential of 3 popular LLMs, namely Bard (Google LLC), ChatGPT-3.5 (OpenAI), and GPT-4 (OpenAI), in medical education to enhance the diagnosis of rare and complex diseases while investigating the impact of prompt engineering on their performance.
METHODS: We conducted experiments on publicly available complex and rare cases to achieve these objectives. We implemented various prompt strategies to evaluate the performance of these models using both open-ended and multiple-choice prompts. In addition, we used a majority voting strategy to leverage diverse reasoning paths within language models, aiming to enhance their reliability. Furthermore, we compared their performance with the performance of human respondents and MedAlpaca, a generative LLM specifically designed for medical tasks.
RESULTS: Notably, all LLMs outperformed the average human consensus and MedAlpaca, with a minimum margin of 5% and 13%, respectively, across all 30 cases from the diagnostic case challenge collection. On the frequently misdiagnosed cases category, Bard tied with MedAlpaca but surpassed the human average consensus by 14%, whereas GPT-4 and ChatGPT-3.5 outperformed MedAlpaca and the human respondents on the moderately often misdiagnosed cases category with minimum accuracy scores of 28% and 11%, respectively. The majority voting strategy, particularly with GPT-4, demonstrated the highest overall score across all cases from the diagnostic complex case collection, surpassing that of other LLMs. On the Medical Information Mart for Intensive Care-III data sets, Bard and GPT-4 achieved the highest diagnostic accuracy scores, with multiple-choice prompts scoring 93%, whereas ChatGPT-3.5 and MedAlpaca scored 73% and 47%, respectively. Furthermore, our results demonstrate that there is no one-size-fits-all prompting approach for improving the performance of LLMs and that a single strategy does not universally apply to all LLMs.
CONCLUSIONS: Our findings shed light on the diagnostic capabilities of LLMs and the challenges associated with identifying an optimal prompting strategy that aligns with each language model\'s characteristics and specific task requirements. The significance of prompt engineering is highlighted, providing valuable insights for researchers and practitioners who use these language models for medical training. Furthermore, this study represents a crucial step toward understanding how LLMs can enhance diagnostic reasoning in rare and complex medical cases, paving the way for developing effective educational tools and accurate diagnostic aids to improve patient care and outcomes.

Background: The interactive journal club is designed to utilize a new approach in appraising research in order to maximize the benefits of the traditional journal club. In this new approach, the participants are actively involved in a structured process of critical data appraisal rather than just being passive listeners. In this case study, we applied the interactive journal club format and assessed its impact among our endocrinology fellows-in-training. Methods: We conducted four interactive journal club sessions within a four-week span, one per each week via a virtual platform. The 12 participants were the same throughout all sessions. Each session was recorded following informed consent. At the end of all sessions, feedback was obtained, tabulated and compared. Results: Sessions lasted from 59 to 83 minutes (mean, 67.75 minutes). Participants became more active and spontaneous as the sessions progressed. All participants found the format more fun and proactive. This approach allowed more critical thinking and processing of information. Salient features include increased self-esteem and confidence, additional learning from other participants, better retention of information, and utilization in future practice. Conclusions: Traditional approaches are transformed from passive presentations of recent developments in medicine into an interactive discussion while allowing the retention of the spirit and essence of a traditional journal club, as well as exploring new and improved approaches in clinical training and education.

UNASSIGNED: To obtain 3D printed bone models with a haptic sensation similar to that of the real bone, which will help the surgeon to learn and improve based on practice.
UNASSIGNED: From computed tomography, 3 digital anatomical models of the human proximal femur were created and, by modifying the printing parameters, both cortical and trabecular tissues were simulated, which were combined in a different cortico-cancellous interface depending on the bone segment. The 3 equivalent models obtained were compared with a commercial Sawbone synthetic model and subjected to a series of blind surgical practice trials performed by 5 TOC specialists from a hospital, each of them with different degrees of expertise. A statistical analysis of the qualitative data collected based on the Wilcoxon test, the Spearman correlation matrix, and the Validity Ratio Coefficient was performed.
UNASSIGNED: The deviations observed in the dimensional study are less than 0.2 millimeter, which confirms the validity of the 3DP-FFF technology to geometrically recreate personalized biomodels with high anatomical precision.
UNASSIGNED: The reproductions obtained have given rise to a reliable method that professionals can refine to plan operations with the consequent reduction of time and risks for the patient, as well as for medical training.

UNASSIGNED: Gastointestinal endoscopy is a complex practical skill, and training and experience are required to ensure the accuracy and safety of the procedures. Therefore, proper endoscopy training is needed to provide highly skilled endoscopists. This study explores the learning experience and assesses the need for endoscopy training in Indonesia from an endoscopy trainee\'s point of view. Limitations from the current training model and the trainees\' suggestions hopefully will become a foundation for the future endoscopy training model in Indonesia.
UNASSIGNED: A total of 132 current endoscopy trainees and graduates of endoscopy training from various centers in Indonesia completed an online qualitative survey regarding their endoscopy training experience, their satisfaction with the current training method, barriers to achieving competency, and their suggestions for future training. Data were subjected to descriptive and qualitative analysis using content analysis.
UNASSIGNED: We found variations in the trainee\'s learning experience regarding the training supervision, feedback, and assessment methods. The most common endoscopy training methods were observation and direct practice with supervision. There was only a low proportion of simulator use (25%). The most found concept in barriers to achieving competency was \"insufficient number of patients.\" Meanwhile, the most found concept in suggestions for future training methods was \"increasing the variety of cases and procedures.\"
UNASSIGNED: Our findings suggest that there are still variations in endoscopy training methods in Indonesia. Therefore, we propose to design a standardized endoscopy training program to ensure the competence of endoscopy trainees and better care for endoscopic patients. Simulators might be used to increase the trainees\' competence in settings with low numbers of patients or cases.

BACKGROUND: Artificial intelligence (AI) is a rapidly developing field with the potential to transform various aspects of health care and public health, including medical training. During the \"Hygiene and Public Health\" course for fifth-year medical students, a practical training session was conducted on vaccination using AI chatbots as an educational supportive tool. Before receiving specific training on vaccination, the students were given a web-based test extracted from the Italian National Medical Residency Test. After completing the test, a critical correction of each question was performed assisted by AI chatbots.
OBJECTIVE: The main aim of this study was to identify whether AI chatbots can be considered educational support tools for training in public health. The secondary objective was to assess the performance of different AI chatbots on complex multiple-choice medical questions in the Italian language.
METHODS: A test composed of 15 multiple-choice questions on vaccination was extracted from the Italian National Medical Residency Test using targeted keywords and administered to medical students via Google Forms and to different AI chatbot models (Bing Chat, ChatGPT, Chatsonic, Google Bard, and YouChat). The correction of the test was conducted in the classroom, focusing on the critical evaluation of the explanations provided by the chatbot. A Mann-Whitney U test was conducted to compare the performances of medical students and AI chatbots. Student feedback was collected anonymously at the end of the training experience.
RESULTS: In total, 36 medical students and 5 AI chatbot models completed the test. The students achieved an average score of 8.22 (SD 2.65) out of 15, while the AI chatbots scored an average of 12.22 (SD 2.77). The results indicated a statistically significant difference in performance between the 2 groups (U=49.5, P<.001), with a large effect size (r=0.69). When divided by question type (direct, scenario-based, and negative), significant differences were observed in direct (P<.001) and scenario-based (P<.001) questions, but not in negative questions (P=.48). The students reported a high level of satisfaction (7.9/10) with the educational experience, expressing a strong desire to repeat the experience (7.6/10).
CONCLUSIONS: This study demonstrated the efficacy of AI chatbots in answering complex medical questions related to vaccination and providing valuable educational support. Their performance significantly surpassed that of medical students in direct and scenario-based questions. The responsible and critical use of AI chatbots can enhance medical education, making it an essential aspect to integrate into the educational system.