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\"This practical guide to biosimulation provides the hands-on experience needed to devise, design and analyze simulations of biophysical processes for applications in biological and biomedical sciences. Through real-world case studies and worked examples, students will develop and apply basic operations through to advanced concepts, covering a wide range of biophysical topics including chemical kinetics and thermodynamics, transport phenomena, and cellular electrophysiology. Each chapter is built around case studies in a given application area, with simulations of real biological systems developed to analyze and interpret data. Open-ended project-based exercises are provided at the end of each chapter, and with all data and computer codes available online (www.cambridge.org/biosim) students can quickly and easily run, manipulate, explore and expand on the examples inside. This hands-on guide is ideal for use on senior undergraduate/graduate courses and also as a self-study guide for anyone who needs to develop computational models of biological systems\"-- Provided by publisher.

Case studies have shown ChatGPT can run clinical simulations at the medical student level. However, no data have assessed ChatGPT's reliability in meeting desired simulation criteria such as medical accuracy, simulation formatting, and robust feedback mechanisms. This study aims to quantify ChatGPT's ability to consistently follow formatting instructions and create simulations for preclinical medical student learners according to principles of medical simulation and multimedia educational technology. Using ChatGPT-4 and a prevalidated starting prompt, the authors ran 360 separate simulations of an acute asthma exacerbation. A total of 180 simulations were given correct answers and 180 simulations were given incorrect answers. ChatGPT was evaluated for its ability to adhere to basic simulation parameters (stepwise progression, free response, interactivity), advanced simulation parameters (autonomous conclusion, delayed feedback, comprehensive feedback), and medical accuracy (vignette, treatment updates, feedback). Significance was determined with χ² analyses using 95% CIs for odds ratios. In total, 100% (n=360) of simulations met basic simulation parameters and were medically accurate. For advanced parameters, 55% (200/360) of all simulations delayed feedback, while the Correct arm (157/180, 87%) delayed feedback was significantly more than the Incorrect arm (43/180, 24%; P<.001). A total of 79% (285/360) of simulations concluded autonomously, and there was no difference between the Correct and Incorrect arms in autonomous conclusion (146/180, 81% and 139/180, 77%; P=.36). Overall, 78% (282/360) of simulations gave comprehensive feedback, and there was no difference between the Correct and Incorrect arms in comprehensive feedback (137/180, 76% and 145/180, 81%; P=.31). ChatGPT-4 was not significantly more likely to conclude simulations autonomously (P=.34) and provide comprehensive feedback (P=.27) when feedback was delayed compared to when feedback was not delayed. These simulations have the potential to be a reliable educational tool for simple simulations and can be evaluated by a novel 9-part metric. Per this metric, ChatGPT simulations performed perfectly on medical accuracy and basic simulation parameters. It performed well on comprehensive feedback and autonomous conclusion. Delayed feedback depended on the accuracy of user inputs. A simulation meeting one advanced parameter was not more likely to meet all advanced parameters. Further work must be done to ensure consistent performance across a broader range of simulation scenarios.

\"Accurate estimation, diagnosis, and prevention of COVID-19 is a global challenge for healthcare organizations. Innovative measures can introduce and implement AI, and Mathematical Modeling applications. This book provides insight into the recent advances of applications, statistical methods, and mathematical modeling for the healthcare industry. This book covers the state-of-the-art applications of AI and Machine Learning in past epidemics, pandemics, and COVID-19. It offers recent global case studies, and discusses how AI and statistical methods, initiatives, and applications such as Machine Learning, Deep Learning, Correlation and Regression Analysis play a major role in the prediction, diagnosis, and prevention of a pandemic. It will also focus on how AI and statistical applications can facilitate and restructure the healthcare system. This book is written for Researchers, Students, Professionals, Executives, and the general public\"-- Provided by publisher.

Background Medical education is adapting to meet the growing demands of healthcare and patient care complexities. Traditional clinical training often relies on limited patient encounters, which may not fully develop clinical competence. Simulation-based medical education (SBME) offers controlled, immersive environments for practicing clinical skills and decision-making without risking patient safety. While SBME has been well-studied in advanced training, its effectiveness in first- and second-year medical students remains underexplored. This study aims to explore and quantify pre-clerkship medical students' perspectives on how SBME impacts confidence in clinical decision-making, communication, and clinical skills, compared to traditional learning methods alone. Methods This pre- and post-survey-based study assessed the impact of simulation (SIM) on students' self-reported confidence in clinical decision-making, communication, and clinical skills. Six simulation scenarios that aligned with the undergraduate medical curriculum of one Canadian institution were conducted from October 2023 to March 2024. Participants completed pre- and post-simulation surveys using 5-point Likert scales. A total of 67 surveys were analyzed. Results  All 67 surveys were analyzed (35 pre-, 32 post-simulation) using one-sided Wilcoxon Signed Rank Tests. Pre-simulation responses indicated low baseline confidence, with only one item rated above neutrality. Post-simulation ratings showed statistically significant improvements across all domains (p < 0.01). Students also reported that they perceived simulation as more effective than traditional didactic learning in preparing them for clinical practice.  Conclusions This exploratory study suggests that simulation-based education can enhance pre-clerkship students' confidence in clinical decision-making, communication, and procedural skills, domains often underdeveloped at this stage of training. These findings offer early evidence that high-fidelity simulation may accelerate perceived clinical readiness. However, due to the small, self-selected sample, non-parallel survey design, and reliance on subjective outcomes, results should be treated as exploratory. Further multi-site studies using objective measures are needed to assess long-term impact on knowledge and skill retention as well as clinical performance.

With the development of science and technology, great changes have taken place in medical education, making it increasingly complicated and diversified. For medical students who have just finished basic medicine courses and are preparing for their hospital internships, it is difficult to gain experience performing direct physical examinations on patients. Currently, residents＇ clinical skills are assessed very strictly; simply taking notes and reciting facts will not suffice. Because considerable attention is being paid to medical students＂ clinical skills on a national level,

Background Many newly qualified doctors feel unprepared for clinical practice. The literature identifies themes including difficulties with clinical reasoning, emergency management, handover, and prioritization of tasks. Although there is an expected level of anxiety for newly qualified doctors, this appears to be amplified with respect to the first on-call shifts that encompass these themes. Materials and methods Virtual on-call (VOC) is a low-fidelity, ward-based simulation for senior undergraduate medical students designed to simulate an on-call in a supported environment with high psychological fidelity. Sessions were provided across two hospital sites for students to attend voluntarily. Three simulation sessions were created, each composed of five medical and surgical scenarios of differing complexity. Students responded to simulated bleeps (pager messages) and attended relevant wards to find patient notes and complete paper-based tasks. A student-led handover concluded the simulation followed by facilitator-led structured feedback and debrief. Students completed pre- and post-session questionnaires collecting quantitative and qualitative feedback. Facilitators received feedback on their teaching. A total of 30 resident doctors volunteered to teach, and 39 students attended at least one session. Results Pre-session questionnaires highlighted that 91% of respondents (n=32) felt scared/nervous/petrified about the idea of their first on-call. Prior to the first VOC session, the baseline assessment highlighted a lack of confidence among medical students regarding on-call working. Post-session results (session one) showed statistically significant increases in confidence in all the themes assessed (paired t-test with statistical significance considered at p<0.05). Forty-seven percent of first-session participants (n=14) felt positive about on-call working after attending VOC. Students who completed multiple sessions continued to have significant increases in their overall confidence levels between sessions. All students who attended three sessions were left feeling positive about their first on-call (n=2). About 95% (n=38) reported a constructive learning environment which was useful to improve preparedness for practice and time management skills. Although students reported finding the experience stressful at times, they remarked how it was beneficial to have \"the opportunity to practice a wide range of skills while in an on-call simulation, how to manage acute situations, how to prioritize, and how to escalate to a senior.\" They reported feeling \"more confident holding the bleep, finding guidance, and seeking guidance.\" Conclusion This program fills an unmet educational need. Feedback was overwhelmingly positive, displaying significantly increased confidence in multiple skills associated with being a safe and successful on-call doctor. We hope that the confidence gained from the on-call program will translate to improved practice when the participants qualify as doctors with a positive impact on patient care.

Background Large language models (LLMs) are increasingly explored in healthcare and education. In medical education, they hold the potential to enhance learning by supporting personalized teaching, resource development, and student engagement. However, LLM use also raises concerns about ethics, accuracy, and reliance. Understanding how educators leverage LLMs can help assess their role and implications in medical education. Methods This cross-sectional online survey was conducted among medical teachers in India from December 2023 to March 2024. A validated questionnaire with acceptable internal consistency and test-retest reliability was used. It collected data on LLM chatbot usage patterns, as well as teachers' knowledge, attitudes, and practices regarding LLMs for educational purposes. Results A total of 396 medical teachers with an average teaching experience of 4.12±2.47 (minimum six months, maximum 13 years) years participated from different parts of India. The majority of the teachers heard about ChatGPT (OpenAI, San Francisco, CA, USA) (85%), followed by Copilot/Bing (Microsoft, Washington, DC, USA) (53%), and Gemini/Bard (Google, Mountain View, CA, USA) (45%) (p-value < 0.0001). However, 29% of the respondents never used it and 47% rarely use LLMs for educational purposes (p-value < 0.0001). The majority of the teachers use it for making any topic simple (55%), generating text for PowerPoint slides (55%), generating multiple-choice questions (MCQs) (52%), and finding answers to student's queries (35%). Knowledge (3.4±0.47) showed the highest score, followed by practice (3.3±0.81) and attitude (3.14±0.46) (p-value = 0.0023). Conclusion While awareness of LLMs was high among medical teachers in India, their actual usage for educational purposes remains limited. Despite recognizing the potential of LLMs for simplifying topics, generating teaching materials, and addressing student queries, a significant proportion of educators seldom integrate these technologies into their teaching practices. Institutions may provide training to help medical educators effectively integrate LLMs into teaching practices.

Purpose Total hip arthroplasty (THA) requires precise implant positioning to ensure long-term success. Herein, we evaluated the effect of virtual reality (VR) on the surgical precision of THA, particularly when used by experienced surgeons. Methods In this single-center, prospective, case-control study, 34 patients who underwent primary THA performed by a single experienced surgeon were divided into the control (without VR simulation) and VR (with VR simulation) groups. Preoperative planning involved the creation of three-dimensional models from computed tomography scans using ZedHip® software (Lexi, Tokyo, Japan). The primary outcomes assessed included the accuracy of implant placement, operative time, and intraoperative blood loss. The secondary outcomes included postoperative hospital stay and in-hospital complications. Results A significant improvement in radiographic inclination (RI) was observed in the VR group as compared to controls. Other surgical parameters, such as radiographic anteversion, operation time, blood loss, and postoperative hospital stay, showed no significant differences between the groups. Discrepancies in planned versus actual implant sizes were noted but were not significantly different between groups. Conclusion VR application in preoperative planning improved the RI accuracy in acetabular cup placement for THA, demonstrating its potential to enhance surgical precision for experienced surgeons. This study highlights the evolving role of VR, from a training tool to an integral part of advanced surgical planning in orthopedics.

Background Stroke is a medical emergency that is risk-stratified using a national scoring system called the National Institute of Health Stroke Scale (NIHSS). The management of an acute stroke necessitates prompt management and swift decision-making. Human factors were identified in the literature as the main rate-limiting step to improving door-to-needle (DTN) time. We felt it would be prudent to design a local stroke course implemented at Great Western Hospital Swindon that incorporates both traditional and simulation-based elements to improve theoretical knowledge and emulate real-life scenarios. The objective of this course was to improve practical application in the efficient assessment and management of stroke patients, as this is critical to delivering timely treatment with thrombolysis or thrombectomy.  Methods Twenty-four medical professionals (medical students and resident doctors) participated in our course between November 2022 and July 2023. The domains assessed included understanding thrombolysis, understanding thrombectomy, confidence in performing NIHSS, and confidence in the assessment of stroke patients. The effectiveness of the stroke simulation course was assessed both quantitatively and qualitatively with pre- and post-course questionnaires. Results There was a significant improvement (p<0.05) in all four assessed domains. There was a significant increase (p=0.0003) in the mean difference of score 3.75 (95% CI: 2.43-5.07) in understanding thrombolysis. Similarly, understanding of thrombectomy was significantly improved (p=0.0002) with a mean difference in score of 3.4 (95% CI: 2.28-4.46). There was also a significant increase (p<0.0001) in confidence in completing NIHSS scoring by a mean of 4.33 (95% CI: 3.55-5.12). Lastly, there was a significant increase (p=0.0012) in the mean by 2.75 (95% CI: 1.51-3.99) in confidence in the assessment of stroke. Overall, 95.8% of the participants found the course at least good, if not very good or excellent, and 91.7% would recommend this course to others. Conclusion We found traditional and simulation-based training to be effective in improving understanding of thrombolysis, understanding of thrombectomy, confidence in NIHSS scoring, and confidence in the assessment of stroke patients. This study validates the effectiveness of our course in improving assessment and management in acute stroke patients. We infer that improvements in these domains coupled with simulation training focused on human factors (e.g., fatigue affecting decision-making or logistical issues such as delays in neuroimaging due to scanner availability) would achieve better DTN time in the participants of our course.

Background First-year medical students may find it challenging to integrate complex physiological concepts, particularly neuromuscular physiology. While concept mapping has shown promise in medical education, its specific application in teaching intricate physiological mechanisms still needs to be explored. With this background, the objective of the study was to assess the feasibility of using concept mapping among first-year medical students and to explore the perception of students about concept mapping as an educational tool. Methods A mixed-methods study was conducted with first-year medical students (n = 110) of the 2023-2024 batch at All India Institute of Medical Sciences, Bhubaneswar, India. A briefing on the basic theories of concept mapping was carried out. Students participated in a structured concept mapping session focusing on the mechanism of muscle contraction. Students (two students in a group) created concept maps illustrating relationships between neuronal architecture, action potentials, neuromuscular transmission, sarcotubular system, excitation-contraction coupling, and muscle contraction processes. Data collection included digital submissions of concept maps and structured feedback questionnaires. Two faculty members evaluated the concept maps, and student feedback was analyzed using quantitative and qualitative approaches. Results A total of 110 first-year undergraduate medical students participated in the study and created 55 concept maps. The students scored 17.32 ± 1.7 out of 20 maximum achievable scores, which corresponds to an average of 86.59%. Seventy-five (68.18%) students rated the technique as excellent, 32 (29.09%) found it good, and only three (2.73%) rated it as average. Ninety-eight (88.8%) students strongly agreed or agreed that it provided a practical learning experience and found it refreshing compared to traditional lectures. Additionally, 102 (92.7%) students acknowledged its ability to stimulate creative thinking, and 102 (92.7%) also reported effective collaboration with peers. According to 82 (74.5%) students, concept mapping also facilitated in-depth knowledge acquisition, demonstrating its effectiveness in promoting active, engaging, and collaborative learning. Qualitative analysis revealed that concept mapping helped the students organize complex information, encourage critical thinking, improve retention through visual learning, promote collaborative knowledge-building, and facilitate self-assessment of their understanding. Conclusion Concept mapping can be used as a pedagogical tool for teaching complex neuromuscular physiology concepts to first-year medical students. The technique can bridge the gap between fragmented knowledge and integrated understanding while promoting active learning and critical thinking. The majority of the students rated it as excellent or good. It significantly enhances engagement, creative thinking, and deeper subject understanding compared to traditional lectures. Qualitative feedback underscores its role in improving comprehension of complex concepts and critical thinking skills.