Clinical Medical Education Research Articles

Why do undergraduate medical students choose medical humanities? A cross-sectional study at an Italian University.

Medical humanities can contribute positively to clinical practice and medical education. Therefore, in many countries, medical schools have been progressively incorporating medical humanities into their curriculum. In Italy, only a few medical schools offer a variety of medical humanities courses, often as elective. What induces Italian medical students to take a medical humanities course has not yet been explored. The aim of this study is to fill this gap by investigating whether academic performance, sociodemographic and psychological variables may influence student motivation. We conducted a cross-sectional study in a medical school and collected data from 260 medical students, from the 3rd to the 6th year of the degree course. The students who chose to take a course in Medical Humanities were compared with those who chose not to take such a course, analysing numeric variables (age, grade point average, psychometric scores) and categorical variables (gender, nationality, educational level, living conditions). Motivations were investigated by open-ended questions and categorized prior to analyses. The two subgroups showed no significant differences in sociodemographic characteristics, except for age, which was lower for the students who chose to take a medical humanities course (p < 0.001). Among the psychometric scores, only the anxiety score differed significantly between the groups, being lower for the students who chose a medical humanities course (p < 0.05). Regarding academic performance, the number of examinations passed was similar between the groups, while the average grade was lower for the students taking the course (p < 0.01). Interest in the humanities and their educational potential were the main reasons for choosing to take a course in medical humanities (76.2%). Concurrent commitments and lack of time were the major obstacles to this choice (39%). Age, anxiety levels and academic performance seem to be inversely associated with the choice to take a medical humanities course. Considering the workload due to curricular activities when planning elective courses could increase student participation.

Paying attention to improving the pathology diagnosis and research level of ocular tumors

Ocular tumor pathology, an important part of ophthalmic pathology, plays a supporting role in understanding the characteristics of ocular tumors and making decisions of the diagnosis and treatment. With the rapid development of ocular oncology in China, the need for specialization and standardization of ocular tumor pathology has become increasingly prominent. Based on the advancement and application of ocular tumor pathology diagnostic techniques, this article points out the main issues in the pathological diagnosis of ocular tumors in China and emphasizes the importance of improving the pathological diagnosis and research level of ocular tumors via different methods and pathways, such as accelerating the construction of ocular tumor pathology research platforms and bases, cultivating professional teams of ocular tumor pathology diagnostic talents, and promoting the scientific research and clinical medical education in ocular tumor pathology. The aim is to provide guidance for a comprehensive improvement of the pathological diagnostic capabilities for ocular tumors in China.

Bibliometric analysis of scholarly publications related to family medicine in Thailand

BackgroundFamily medicine has been recognized as a medical specialty in Thailand. However, there is a lack of information about scholarly publications. Bibliometric analysis is a valuable methodology for identifying research trends and knowledge gaps in this field. This study aims to analyze scholarly publications related to family medicine in Thailand. MethodThis bibliometric analysis was conducted based on the PubMed search in March 2023. All relevant literature related to 'family medicine' and 'Thailand' was retrieved. Performance analysis examined the publication trends from 1963 to 2023, the top 10 journals publishing relevant articles, and the number of publications by research areas. Science mapping depicted clusters of co-occurring author keywords, their relationships, and the research themes' trend over two decades. ResultsA total of 1,483 publications were retrieved. The number of publications showed an upward trend, increasing from 61 before 2000 to 359 in 2020. The Journal of the Medical Association of Thailand was the leading journal, publishing 267 articles (18.0%). Research areas were categorized into Basic Knowledge (n=211), Clinical Problem Solving (n=714), Health Service (n=256), Health System (n=254), and Medical Education (n=48). Science mapping identified six clusters based on 106 co-occurrence keywords, including public health, health services, medical issues in older adults, healthcare access, epidemiology, and others. Research themes have shifted from infectious diseases and public health to primary care and non-communicable diseases. ConclusionFuture research should focus more on implementation at a population level and healthcare system, with more investigation into geriatric care and child and maternal health.

Racial biases in clinical practice and medical education: a scoping review

IntroductionHealth inequalities represent a major challenge in contemporary medicine, with some attributed to racial biases. Recently, in the United States, a call to combat discrimination in the field of health has resonated, particularly in the context of the COVID-19 crisis, in which minorities have been disproportionately affected. These calls echo recommendations from the Institute of Medicine dating back to 2001, urging the fight against inequalities in access to health care. In France, inequalities based on presumed origin persist, yet medical education on these issues is virtually nonexistent.Materials and MethodsWe conducted a scoping review literature review in accordance with the JBI recommendations for scoping review writing and applied the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist to explore existing conceptual and educational data on racial biases in medicine. The inclusion criteria were interventional studies or evaluations of existing educational programs in medical training addressing the fight against racial biases in clinical settings published in French or English between 2003 and 2023. Six databases were systematically consulted.ResultsOut of 748 initial studies, 28 were included in our study. The median number of participants in the studies was low, interventions were diverse, and participants were generally well received, most of whom were self-selected. No study has evaluated the clinical impact of these interventions. The highlighted concepts included levels of racism, cultural competence, cultural humility, and critical race theory.DiscussionThe authors most frequently referred to institutionalized racism, demonstrating the systemic nature of these issues. At an individual level, implicit and unconscious biases were most often emphasized. It appears that the concept of “race” is a sociopolitical construct without supporting biological determinants. Humility is central to this field of study, as it encourages questioning of individual or collective medical practices. In France, the context, including the prohibition of ethnic statistics, may hinder the objectification of discrimination. Immersion and virtual patient scenarios emerged as potential solutions for evaluating the clinical impact of interventions. A more horizontal medical pedagogy seems better suited to teach these sensitive issues.

Exploring context and culture in clinical reasoning medical education: A qualitative exploratory study.

Clinical reasoning processes are complex and interwoven with culture and context. While these relationships have been explored to understand the outcomes of clinical reasoning, there has been little exploration of how to integrate these relationships when teaching and learning clinical reasoning. Using semi-structured interviews, this research explored the role of context and culture in clinical reasoning medical education. Participants were clinical teachers recruited from across Northern Ontario. The data were analysed independently by two reviewers using both thematic analysis and critical discourse analysis, and peer reviewed by a third researcher. The role of context and culture is inherent to the personal, professional and pedagogical aspects of clinical reasoning, especially when teaching about the complexities of Northern Ontario. The major themes that came through were: 1) teaching and learning clinical reasoning needs reflexivity, 2) developing clinical reasoning skills needs time and 3) clinical reasoning pedagogy should acknowledge and encompass practice variation and patient diversity. Teaching clinical reasoning in Northern Ontario involves being aware of the complexities that are inherent in interacting with patients and communities. Through personal, professional and pedagogical models, the students and teachers can address the complexities of cultural and contextual clinical reasoning.

Perceptions of ChatGPT in healthcare: usefulness, trust, and risk.

This study explores the perceptions of ChatGPT in healthcare settings in Taiwan, focusing on its usefulness, trust, and associated risks. As AI technologies like ChatGPT increasingly influence various sectors, their potential in public health education, promotion, medical education, and clinical practice is significant but not without challenges. The study aims to assess how individuals with and without healthcare-related education perceive and adopt ChatGPT, contributing to a deeper understanding of AI's role in enhancing public health outcomes. An online survey was conducted among 659 university and graduate students, all of whom had prior experience using ChatGPT. The survey measured perceptions of ChatGPT's ease of use, novelty, usefulness, trust, and risk, particularly within clinical practice, medical education, and research settings. Multiple linear regression models were used to analyze how these factors influence perception in healthcare applications, comparing responses between healthcare majors and non-healthcare majors. The study revealed that both healthcare and non-healthcare majors find ChatGPT more useful in medical education and research than in clinical practice. Regression analysis revealed that for healthcare majors, general trust is crucial for ChatGPT's adoption in clinical practice and influences its use in medical education and research. For non-healthcare majors, novelty, perceived general usefulness, and trust are key predictors. Interestingly, while healthcare majors were cautious about ease of use, fearing it might increase risk, non-healthcare majors associated increased complexity with greater trust. This study highlights the varying expectations between healthcare and non-healthcare majors regarding ChatGPT's role in healthcare. The findings suggest the need for AI applications to be tailored to address specific user needs, particularly in clinical practice, where trust and reliability are paramount. Additionally, the potential of AI tools like ChatGPT to contribute to public health education and promotion is significant, as these technologies can enhance health literacy and encourage behavior change. These insights can inform future healthcare practices and policies by guiding the thoughtful and effective integration of AI tools like ChatGPT, ensuring they complement clinical judgment, enhance educational outcomes, support research integrity, and ultimately contribute to improved public health outcomes.

Simulation Training of Medical Workers of Tyumen Region in Emergency Care within the Framework of the Project "Attention! Children's Life!"

According to the Global consensus statement on simulation in healthcare, developed by the world's leading experts in the field, it is necessary to integrate simulation training into everyday clinical practice and medical education. The simulation should also be available to specialists working in rural areas, as well as supported by the health system and educational institutions. In Tyumen region, as part of the implementation in 2023-2024 of the project "Attention! Children's life!" conducted simulation training of emergency care for children for pediatricians, paramedics, nurses.

A systematic review of the use of FHIR to support clinical research, public health and medical education

PurposeThis systematic review aimed to identify and categorize applications using Fast Healthcare Interoperability Resources (FHIR) to support activities outside of direct healthcare provision.Design/methodology/approachA systematic electronic search was performed, and 53 studies were included after the selection process.FindingsThe results show that FHIR is being used to support (1) clinical research (i.e. clinical research based on interventional trials, data interoperability to support clinical research and advanced communication services to support clinical research), (2) public health and (3) medical education. Despite the FHIR potential to support activities outside of direct healthcare provision, some barriers were identified, namely difficulties translating the proposed applications to clinical environments or FHIR technical issues that require further developments.Originality/valueThis study provided a broad review of how FHIR is being applied in clinical activities outside of direct clinical care and identified three major domains, that is, clinical research, public health and medical education, being the first and most representative in terms of number of publications.

Stanford Network for Advancement and Promotion: The impact of a community building-focused leadership development program on the success of underrepresented groups in academic medicine.

Leadership can be an isolating experience and leaders from underrepresented groups (URGs) may experience even greater isolation and vulnerability because of lack of representation. Given the collaborative nature of medicine, leadership programs for physicians need to address isolation. Social support is one mechanism to combat this isolation; however, most leadership programs focus exclusively on skills building. The Stanford Network for Advancement and Promotion (SNAP) program was developed to reduce isolation among physician leaders from URGs in academic medicine leadership by building a supportive network of peers. Ten women physicians from diverse racial/ethnic backgrounds were invited to participate in SNAP. Annual surveys were administered to participants to assess the effectiveness of SNAP on decreasing feelings of isolation and increasing professional leadership growth. The authors charted the expansion and adaptation of the program model across gender and in additional settings. SNAP effectively created a sense of community among the physician leaders. Participants also reported feeling challenged by the program and that they had grown in terms of critical thinking, organizational knowledge, and empowerment as leaders. Participants found community building to be the most valuable program component. Because of this success, the SNAP model has been adapted to create 10 additional cohorts. Leadership programs like SNAP that focus on reducing isolation are instrumental for retaining and promoting the career advancement of physicians from URGs. Developing a diverse workforce of academic physicians is essential to providing high-quality and equitable clinical care, research, and medical education.

Development of a Patient-Centered Pathway of Care for Retinoblastoma Patients: A Mixed-Methods Pilot Study.

Introduction Retinoblastoma treatment and follow-up is complex and varies between patients. Pathways of care can enhance quality of care, patient outcomes, safety, satisfaction, and resource optimization. Developing a pathway of care for retinoblastoma was identified as a top research priority by the retinoblastoma community. This study aimed to co-design and pilot a pathway of care called the "Retinoblastoma Journey Map" tailored for caregivers of newly diagnosed children with retinoblastoma. Methods A working group of patients, health professionals and researchers used human-centred design to ideate, prototype and refine the Retinoblastoma Journey Map. Caregivers of affected children were recruited to use and evaluate the Map. Mixed-methods data was collected on feasibility, acceptability, usability and perceived impact on communication, self-efficacy, anxiety, depression, and the quality of physician-patient interaction. Results The Retinoblastoma Journey Map consisted of an illustrated roadmap with 25 child-friendly stickers covering clinical treatment, medical education and milestones. Quantitative analysis revealed that the Map was feasible, acceptable, and usable; however, no significant effect on communication, self-efficacy, anxiety, depression or quality of physician-patient interaction was observed. Qualitative analysis identified 6 themes: Primary Use, Challenges, Impact, Limitations, Feasibility, Acceptability and Usability, and Unmet Needs. Conclusion A pathway of care for retinoblastoma, co-designed by researchers, health professionals and patients, was usable, acceptable, and feasible by caregivers of children with retinoblastoma. While significant effects on communication and physician-patient interaction were not observed, 'legacy building' - documentation of the pathway of care by families for later education of their child - emerged as an unanticipated yet important use of the Retinoblastoma Journey Map.

The Impact of the COVID-19 Pandemic on Ophthalmology Residency and Fellowship Training: A Retrospective Study.

Background The COVID-19 pandemic has negatively affected many aspects of the healthcare system. Many meta-analysis studies showed that surgical training and medical education have faced the most negative effects. Aim In this study, we aim to study the impact of the COVID-19 pandemic on residency and fellowship training in terms of clinical load, surgical exposure, medical education, and research opportunities. Methodology This retrospective study was conducted at King Khaled Eye Specialist Hospital (KKESH), Riyadh, Saudi Arabia, to assess the effects of COVID-19 on the training program by comparing the years 2018 and 2019 before the COVID-19 pandemic to 2020 and 2021 after the COVID-19 pandemic. The sample was inclusive, including 117 KKESH graduates (residents and fellows in the surgical subspecialties) from 2018 to 2021. All the sample populations were included. The data was collected in a specifically designed form. All participants were ensured to have a full surgical logbook with no missing data in the documentation. Outpatient visits were collected through electronic records in the hospital registry. The teaching activities and grand rounds were collected through the residency and fellowship program. Results During 2018-2021, 18,669 surgeries were performed. The total number of surgeries performed was 3,980, 4,898, 4,813, and 4,978 in 2018, 2019, 2020, and 2021, respectively. There was a 23.1% (N = 918) increase in the number of surgeries done by trainees from 2018 to 2019, then a 1.7% (N = 85) reduction from 2019 to 2020, followed by an increase of 3.4% (N = 165) from 2020 to 2021. The mean number of surgeries performed by fellows showed a 25.2% (N = 1,042) increase from 2018 to 2019, a 7.3% (N = 107) reduction from 2019 to 2020, and a 15.1% (N = 550) increase (p = 0.018). In the case of residents, there was a 10.7% (N = 136) reduction from 2018 to 2019, a 2.1% (N = 24) increase from 2019 to 2020, and a 40% (N = 783) reduction in the total number of phacoemulsification surgeries (p < 0.001).In total, there was a 25.1% (N = 8,215) increase in the number of patients seen in outpatient clinics from 2019 to 2020. All activities were on-site during 2018 and 2019. A gradual shift occurred from on-site to virtual over 2020 and 2021 without any effect on the number. From 2019 to 2020, there was an increase in the number of papers submitted by the trainees. There was an increase of 25% (N = 10), 20% (N = 2), and 6.3% (N = 3) in the retrospective research, prospective research, and case reports, respectively. Conclusions The surgical exposure has not affected the fellows and residents of the hospital. However, the number of surgeries for the residents has been affected due to the stoppage of overseas surgical courses during the pandemic. The volume of the outpatient clinic has increased after the pandemic, which could be caused by the increased number of referrals to our tertiary hospital, mainly after the pandemic effect on other hospitals in the kingdom and the implementation of the virtual clinic and telephone call. Interestingly, research activity has also increased after the pandemic.

ChatGPT Versus National Eligibility cum Entrance Test for Postgraduate (NEET PG).

Introduction With both suspicion and excitement, artificial intelligence tools are being integrated into nearly every aspect of human existence, including medical sciences and medical education. The newest large language model (LLM) in the class of autoregressive language models is ChatGPT. While ChatGPT's potential to revolutionize clinical practice and medical education is under investigation, further research is necessary to understand its strengths and limitations in this field comprehensively. Methods Two hundred National Eligibility cum Entrance Test for Postgraduate 2023 questions were gathered from various public education websites and individually entered into Microsoft Bing (GPT-4 Version 2.2.1). Microsoft Bing Chatbot is currently the only platform incorporating all of GPT-4's multimodal features, including image recognition. The results were subsequently analyzed. Results Out of 200 questions, ChatGPT-4 answered 129 correctly. The most tested specialties were medicine (15%), obstetrics and gynecology (15%), general surgery (14%), and pathology (10%), respectively. Conclusion This study sheds light on how well the GPT-4 performs in addressing the NEET-PG entrance test. ChatGPT has potential as an adjunctive instrument within medical education and clinical settings. Its capacity to react intelligently and accurately in complicated clinical settings demonstrates its versatility.

Is ChatGPT ready for primetime? Performance of artificial intelligence on a simulated Canadian urology board exam.

Generative artificial intelligence (AI) has proven to be a powerful tool with increasing applications in clinical care and medical education. ChatGPT has performed adequately on many specialty certification and knowledge assessment exams. The objective of this study was to assess the performance of ChatGPT 4 on a multiple-choice exam meant to simulate the Canadian urology board exam. Graduating urology residents representing all Canadian training programs gather yearly for a mock exam that simulates their upcoming board-certifying exam. The exam consists of written multiple-choice questions (MCQs) and an oral objective structured clinical examination (OSCE). The 2022 exam was taken by 29 graduating residents and was administered to ChatGPT 4. ChatGPT 4 scored 46% on the MCQ exam, whereas the mean and median scores of graduating urology residents were 62.6%, and 62.7%, respectively. This would place ChatGPT's score 1.8 standard deviations from the median. The percentile rank of ChatGPT would be in the sixth percentile. ChatGPT scores on different topics of the exam were as follows: oncology 35%, andrology/benign prostatic hyperplasia 62%, physiology/anatomy 67%, incontinence/female urology 23%, infections 71%, urolithiasis 57%, and trauma/reconstruction 17%, with ChatGPT 4's oncology performance being significantly below that of postgraduate year 5 residents. ChatGPT 4 underperforms on an MCQ exam meant to simulate the Canadian board exam. Ongoing assessments of the capability of generative AI is needed as these models evolve and are trained on additional urology content.

A Comparative Analysis of ChatGPT, ChatGPT-4, and Google Bard Performances at the Advanced Burn Life Support Exam.

Artificial intelligence and large language models (LLMs) have recently gained attention as promising tools in various healthcare domains, offering potential benefits in clinical decision-making, medical education, and research. The Advanced Burn Life Support (ABLS) program is a didactic initiative endorsed by the American Burn Association, aiming to provide knowledge on the immediate care of severely burn patients. The aim of the study was to compare the performance of three LLMs (ChatGPT-3.5, ChatGPT-4, and Google Bard) on the ABLS exam. The ABLS exam consists of 50 questions with 5 multiple-choice answers. The passing threshold is 80% of correct answers. The 3 LLMs were queried with the 50 questions included in the latest version of the ABLS exam, on July 18th, 2023. ChatGPT-3.5 scored 86% (43 out of 50), ChatGPT-4 scored 90% (45 out of 50), and Bard scored 70% (35 out of 50). No difference was measured between ChatGPT-3.5 and ChatGPT-4 (P = .538) and between ChatGPT-3.5 and Bard (P = .054), despite the borderline P-value. ChatGPT-4 performed significantly better than Bard (P = .012). Out of the 50 questions, 78% (n = 39) were direct questions, while 12% (n = 11) were presented as clinical scenarios. No difference in the rate of wrong answers was found based on the type of question for the 3 LLMs. ChatGPT-3.5 and ChatGPT-4 demonstrated high accuracy at the ABLS exam and outperformed Google Bard. However, the potential multiple applications of LLMs in emergency burn and trauma care necessitate appropriate surveillance and most likely should represent a tool to complement human cognition.

Enhancing MRI image retrieval using autoencoder-based deep learning: A solution for efficient clinical and teaching applications

BackgroundMagnetic resonance imaging (MRI) image retrieval holds significant value in clinical contexts and medical education due to the shortcomings of traditional methods: slow speed, low accuracy, and limited learning capabilities. Improving this retrieval process is crucial for enhancing medical diagnostics and educational outcomes. Efforts to overcome these challenges are paramount for advancing healthcare practices and educational methodologies. MethodThis study explores the use of autoencoders in deep learning to effectively retrieve MRI images from databases for medical education, emphasizing the model's capacity to be trained with a small amount of labeled data. This work intends to improve the MRI image retrieval process by utilizing autoencoders, demonstrating the promise of deep learning technologies in medical image analysis without the need for large labeled datasets for training. ResultsResearch has demonstrated the exceptional advantages of this method for MRI image retrieval tasks, with an average accuracy of 99.09%. This indicates that the technique performs exceptionally well in this particular domain and is very effective and reliable in extracting MRI images. ConclusionsThis innovative approach can improve the archival management and diagnostic functions of medical images, providing an efficient and reliable solution for MRI image retrieval. It not only helps doctors with clinical diagnosis and medical teaching and research more quickly but also suggests a convenient solution for file management related to medical images.

Potential of Large Language Models in Health Care: Delphi Study.

A large language model (LLM) is a machine learning model inferred from text data that captures subtle patterns of language use in context. Modern LLMs are based on neural network architectures that incorporate transformer methods. They allow the model to relate words together through attention to multiple words in a text sequence. LLMs have been shown to be highly effective for a range of tasks in natural language processing (NLP), including classification and information extraction tasks and generative applications. The aim of this adapted Delphi study was to collect researchers' opinions on how LLMs might influence health care and on the strengths, weaknesses, opportunities, and threats of LLM use in health care. We invited researchers in the fields of health informatics, nursing informatics, and medical NLP to share their opinions on LLM use in health care. We started the first round with open questions based on our strengths, weaknesses, opportunities, and threats framework. In the second and third round, the participants scored these items. The first, second, and third rounds had 28, 23, and 21 participants, respectively. Almost all participants (26/28, 93% in round 1 and 20/21, 95% in round 3) were affiliated with academic institutions. Agreement was reached on 103 items related to use cases, benefits, risks, reliability, adoption aspects, and the future of LLMs in health care. Participants offered several use cases, including supporting clinical tasks, documentation tasks, and medical research and education, and agreed that LLM-based systems will act as health assistants for patient education. The agreed-upon benefits included increased efficiency in data handling and extraction, improved automation of processes, improved quality of health care services and overall health outcomes, provision of personalized care, accelerated diagnosis and treatment processes, and improved interaction between patients and health care professionals. In total, 5 risks to health care in general were identified: cybersecurity breaches, the potential for patient misinformation, ethical concerns, the likelihood of biased decision-making, and the risk associated with inaccurate communication. Overconfidence in LLM-based systems was recognized as a risk to the medical profession. The 6 agreed-upon privacy risks included the use of unregulated cloud services that compromise data security, exposure of sensitive patient data, breaches of confidentiality, fraudulent use of information, vulnerabilities in data storage and communication, and inappropriate access or use of patient data. Future research related to LLMs should not only focus on testing their possibilities for NLP-related tasks but also consider the workflows the models could contribute to and the requirements regarding quality, integration, and regulations needed for successful implementation in practice.

"Incorporating large language models into academic neurosurgery: embracing the new era".

This correspondence examines how LLMs, such as ChatGPT, have an effect on academic neurosurgery. It emphasises the potential of LLMs in enhancing clinical decision-making, medical education, and surgical practice by providing real-time access to extensive medical literature and data analysis. Although this correspondence acknowledges the opportunities that come with the incorporation of LLMs, it also discusses challenges, such as data privacy, ethical considerations, and regulatory compliance. Additionally, recent studies have assessed the effectiveness of LLMs in perioperative patient communication and medical education, and stressed the need for cooperation between neurosurgeons, data scientists, and AI experts to address these challenges and fully exploit the potential of LLMs in improving patient care and outcomes in neurosurgery.

Impact of COVID-19 pandemic on teaching learning process in pre clinical medical education in selective medical college in Bangladesh

Background: The corona virus disease (COVID-19) pandemic has caused major disruption in educational sector including medical education. This disease outbreak has changed the medical education curriculum from traditional platform to elearning for students including Bangladesh. Objective: To observe the impact of COVID-19 pandemic on teaching learning process in medical education in Bangladesh. Methods: This qualitative survey was done among the 68 pre-clinical students aged 19 to 23years, of a non-government medical college in Bangladesh, from march to December 2021. All study participants were first year undergraduate MBBS students from anatomy, biochemistry and physiology. Data was collected by administering pre-tested questionnaires through Google form. Results: About 75% of the participants agreed that online classes were helpful during the home stay period of COVID pandemic, but about 92.2% of them were skeptical about the success of this online system for their future medical education. About 80.9% had difficulty in keeping their concentration during online class. About 94% found difficulty to follow online practical classes. Majority of the students (82.4%) feel anxious whether they were capable of doing their job properly since this method needs technical knowledge. About 50% students had limited access to internet, there were technical issues like power cut (76.5%), and poor network of internet (69.1%) also reduced the effectiveness of online education. Online class also disrupt teacher-student relationship. Conclusion: The internet based classes served the purpose of continuation of academic activity in medical education during adverse condition of COVID . But due to uneven use of technology it had some obstacles also. Student’s ability to learning in medical education was also hampered in the absence of physical classes. This online method of education system needs much improvement to be effective in medical education. J Bangladesh Soc Physiol 2022;17(2): 64-71

MDT 模式+PBL 教学在国内医学临床教学中的应用研究

Clinical practice teaching is a fundamental part of medical education, playing a crucial role in cultivating high-level, innovative, and high-quality medical talents. PBL teaching breaks the traditional indoctrination teaching method, allowing students to play a leading role in the teaching process, while MDT mode teaching fully integrates and utilizes the clinical teaching resources of hospitals, conducts multidisciplinary and multi angle teaching analysis of real clinical data of patients, and connects fragmented important knowledge points. The advantages of MDT mode and PBL teaching are complementary, deeply integrated, and jointly applied to integrate the optimal resources and strength of clinical teaching. The MDT mode and PBL teaching method have achieved certain results in medical clinical teaching. This article reviews the origin and development of MDT mode teaching and PBL teaching, as well as the situation of medical clinical teaching implementation and application in China, analyzes their existing problems and future development, In order to provide experience and inspiration for the implementation of MDT mode+PBL clinical practice teaching in China.

Whole-Slide Imaging (WSI) Versus Traditional Microscopy (TM) Through Evaluation of Parameters in Oral Histopathology: A Pilot Study.

histopathology plays a pivotal role in clinical diagnosis, research, and medical education. In recent years, whole slide imaging (wsi) has emerged as a potential alternative to traditional microscopy for pathological examination. This study aims to provide a comprehensive comparison of wsi and traditional microscopy(tm) in various aspects of histopathology practice. In this study, total of 30 cases comprising of oral premalignant and malignant cases which were diagnostically challenging was considered from the archives of the institute for validation. The slides were scanned with slide scanner and were evaluated by histopathologists. The comparative parameters which were noted were diagnostic discordances, number of fields observed to reach the diagnosis and time taken. The mean time taken by the pathologists to reach the diagnosis was significantly less in whole slide imaging technique. The average number of fields observed was higher by using wsi that too in a lesser time compared to tm, the results were found to be statistically significant with p=0.001.however the diagnostic disparity were seen to be maximum for verrucous lesions both in wsi and tm. wsi has facilitated the specialty with rapid mode of diagnosis in a more efficient and error less manner. It has also aided in case banking as well as research possibilities. Hence with the advent of telepathology it is very much necessary to get trained with wsi as early as possible so that the professionals can render correct diagnosis.