Use In Medical Education Research Articles

AI-based medical ethics education: examining the potential of large language models as a tool for virtue cultivation

BackgroundWith artificial intelligence (AI) increasingly revolutionising medicine, this study critically evaluates the integration of large language models (LLMs), known for advanced text processing and generation capabilities, in medical ethics education, focusing on promoting virtue. Positing LLMs as central to mimicking nuanced human communication, it examines their use in medical education and the ethicality of embedding AI in such contexts.MethodUsing a hybrid approach that combines principlist and non-principlist methodologies, we position LLMs as exemplars and advisors.ResultsWe discuss the imperative for including AI ethics in medical curricula and its utility as an educational tool, identify the lack of educational resources in medical ethics education, and advocate for future LLMs to mitigate this problem as a “second-best” tool. We also emphasise the critical importance of instilling virtue in medical ethics education and illustrate how LLMs can effectively impart moral knowledge and model virtue cultivation. We address expected counter-arguments to using LLMs in this area and explain their profound potential to enrich medical ethics education, including facilitating the acquisition of moral knowledge and developing ethically grounded practitioners.ConclusionsThe study involved a comprehensive exploration of the function of LLMs in medical ethics education, positing that tools such as ChatGPT can profoundly enhance the learning experience in the future. This is achieved through tailored, interactive educational encounters while addressing the ethical nuances of their use in educational settings.

UCFM as the forerunner in the field of Laboratory Animal Science in Sri Lanka

Animals have been models for humans since the dawn of medicine as per the first medical handbook ‘Corpus Hippocraticum’ (around 400 B.C). In an era where no anaesthetic agents were identified, live animals such as dogs, pigs and monkeys were the subjects for vivisections to study human anatomy. Reverend Stephen Hales (1677–1761) inserted a brass pipe into a major blood vessel in the neck of a live horse through which a glass tube was sent and introduced the concept of blood pressure to the scientific fraternity (van Zutphen, 2001; Lewis, 1994; Felts, 1977). Similar to these, different animals have been the models for development of many fields of medicine especially for the benefit of humans. The objective of this article is to describe the role played by the Faculty of Medicine, University of Colombo (UCFM) as the forerunner in laboratory animal science in Sri Lanka to facilitate laboratory animal use in medical education, research, ethics and laboratory animal science education.

Analysis of ChatGPT-3.5’s Potential in Generating NBME-Standard Pharmacology Questions: What Can Be Improved?

ChatGPT by OpenAI is an AI model designed to generate human-like responses based on diverse datasets. Our study evaluated ChatGPT-3.5’s capability to generate pharmacology multiple-choice questions adhering to the NBME guidelines for USMLE Step exams. The initial findings show ChatGPT’s rapid adoption and potential in healthcare education and practice. However, concerns about its accuracy and depth of understanding prompted this evaluation. Using a structured prompt engineering process, ChatGPT was tasked to generate questions across various organ systems, which were then reviewed by pharmacology experts. ChatGPT consistently met the NBME criteria, achieving an average score of 13.7 out of 16 (85.6%) from expert 1 and 14.5 out of 16 (90.6%) from expert 2, with a combined average of 14.1 out of 16 (88.1%) (Kappa coefficient = 0.76). Despite these high scores, challenges in medical accuracy and depth were noted, often producing “pseudo vignettes” instead of in-depth clinical questions. ChatGPT-3.5 shows potential for generating NBME-style questions, but improvements in medical accuracy and understanding are crucial for its reliable use in medical education. This study underscores the need for AI models tailored to the medical domain to enhance educational tools for medical students.

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.

Transforming Applied Medical Sciences: The Impact of AI, VR, and AR on Research, Education Technology, and Clinical Practices

Background: The integration of Educational Technology (EdTech) into applied medical sciences is reshaping research methods, educational practices, and clinical procedures. Technologies such as artificial intelligence (AI), virtual reality (VR), and augmented reality (AR) are increasingly adopted to enhance these fields, though empirical evidence on their overall impact remains limited. Objective: This study aims to evaluate the effectiveness of EdTech innovations in applied medical sciences, focusing on their impact on research productivity, student engagement, and clinical accuracy through both quantitative and qualitative analysis. Method: A mixed-methods approach was employed, combining quantitative surveys with qualitative interviews. Surveys were distributed to 318 researchers and educators, gathering data from January 2022 to June 2024 on the adoption and impact of AI, VR, and AR technologies. In-depth interviews provided additional qualitative insights. Statistical analysis, including t-tests with a significance level of p < 0.05, was used to assess the data. Results: Quantitative survey results showed that the implementation of AI and data analytics led to a 30% increase in research productivity (p < 0.01), with significant improvements in diagnostic accuracy and predictive capabilities. VR and AR use in medical education resulted in a 25% rise in student engagement (p < 0.05) and a 40% improvement in procedural skill retention (p < 0.01). Qualitative insights reinforced these findings, highlighting a 20% increase in collaborative research and improved interdisciplinary communication. EdTech tools also contributed to a 15% enhancement in clinical accuracy (p < 0.05). Conclusions: The integration of EdTech innovations significantly improves research efficiency, educational quality, and clinical outcomes in applied medical sciences. The mixed-methods approach provides a comprehensive evaluation, revealing both quantifiable benefits and deeper insights into the transformative potential of these technologies.

Current Status of ChatGPT Use in Medical Education: Potentials, Challenges, and Strategies.

ChatGPT, a generative pretrained transformer, has garnered global attention and sparked discussions since its introduction on November 30, 2022. However, it has generated controversy within the realms of medical education and scientific research. This paper examines the potential applications, limitations, and strategies for using ChatGPT. ChatGPT offers personalized learning support to medical students through its robust natural language generation capabilities, enabling it to furnish answers. Moreover, it has demonstrated significant use in simulating clinical scenarios, facilitating teaching and learning processes, and revitalizing medical education. Nonetheless, numerous challenges accompany these advancements. In the context of education, it is of paramount importance to prevent excessive reliance on ChatGPT and combat academic plagiarism. Likewise, in the field of medicine, it is vital to guarantee the timeliness, accuracy, and reliability of content generated by ChatGPT. Concurrently, ethical challenges and concerns regarding information security arise. In light of these challenges, this paper proposes targeted strategies for addressing them. First, the risk of overreliance on ChatGPT and academic plagiarism must be mitigated through ideological education, fostering comprehensive competencies, and implementing diverse evaluation criteria. The integration of contemporary pedagogical methodologies in conjunction with the use of ChatGPT serves to enhance the overall quality of medical education. To enhance the professionalism and reliability of the generated content, it is recommended to implement measures to optimize ChatGPT's training data professionally and enhance the transparency of the generation process. This ensures that the generated content is aligned with the most recent standards of medical practice. Moreover, the enhancement of value alignment and the establishment of pertinent legislation or codes of practice address ethical concerns, including those pertaining to algorithmic discrimination, the allocation of medical responsibility, privacy, and security. In conclusion, while ChatGPT presents significant potential in medical education, it also encounters various challenges. Through comprehensive research and the implementation of suitable strategies, it is anticipated that ChatGPT's positive impact on medical education will be harnessed, laying the groundwork for advancing the discipline and fostering the development of high-caliber medical professionals.

Ethics of Writing Personal Statements and Letters of Recommendations with Large Language Models.

Large language models are becoming ubiquitous in the editing and generation of written content and are actively being explored for their use in medical education. The use of artificial intelligence (AI) engines to generate content in academic spaces is controversial and has been meet with swift responses and guidance from academic journals and publishers regarding the appropriate use or disclosure of use of AI engines in professional writing. To date, there is no guidance to applicants of graduate medical education programs in using AI engines to generate application content-primarily personal statements and letters of recommendation. In this Perspective, we review perceptions of using AI to generate application content, considerations for the impact of AI in holistic application review, ethical challenges regarding plagiarism, and AI text classifiers. Finally, included are recommendations to the graduate medical education community to provide guidance on use of AI engines in applications to maintain the integrity of the application process in graduate medical education.

Performance of ChatGPT Across Different Versions in Medical Licensing Examinations Worldwide: Systematic Review and Meta-Analysis.

Over the past 2 years, researchers have used various medical licensing examinations to test whether ChatGPT (OpenAI) possesses accurate medical knowledge. The performance of each version of ChatGPT on the medical licensing examination in multiple environments showed remarkable differences. At this stage, there is still a lack of a comprehensive understanding of the variability in ChatGPT's performance on different medical licensing examinations. In this study, we reviewed all studies on ChatGPT performance in medical licensing examinations up to March 2024. This review aims to contribute to the evolving discourse on artificial intelligence (AI) in medical education by providing a comprehensive analysis of the performance of ChatGPT in various environments. The insights gained from this systematic review will guide educators, policymakers, and technical experts to effectively and judiciously use AI in medical education. We searched the literature published between January 1, 2022, and March 29, 2024, by searching query strings in Web of Science, PubMed, and Scopus. Two authors screened the literature according to the inclusion and exclusion criteria, extracted data, and independently assessed the quality of the literature concerning Quality Assessment of Diagnostic Accuracy Studies-2. We conducted both qualitative and quantitative analyses. A total of 45 studies on the performance of different versions of ChatGPT in medical licensing examinations were included in this study. GPT-4 achieved an overall accuracy rate of 81% (95% CI 78-84; P<.01), significantly surpassing the 58% (95% CI 53-63; P<.01) accuracy rate of GPT-3.5. GPT-4 passed the medical examinations in 26 of 29 cases, outperforming the average scores of medical students in 13 of 17 cases. Translating the examination questions into English improved GPT-3.5's performance but did not affect GPT-4. GPT-3.5 showed no difference in performance between examinations from English-speaking and non-English-speaking countries (P=.72), but GPT-4 performed better on examinations from English-speaking countries significantly (P=.02). Any type of prompt could significantly improve GPT-3.5's (P=.03) and GPT-4's (P<.01) performance. GPT-3.5 performed better on short-text questions than on long-text questions. The difficulty of the questions affected the performance of GPT-3.5 and GPT-4. In image-based multiple-choice questions (MCQs), ChatGPT's accuracy rate ranges from 13.1% to 100%. ChatGPT performed significantly worse on open-ended questions than on MCQs. GPT-4 demonstrates considerable potential for future use in medical education. However, due to its insufficient accuracy, inconsistent performance, and the challenges posed by differing medical policies and knowledge across countries, GPT-4 is not yet suitable for use in medical education. PROSPERO CRD42024506687; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=506687.

Quality of Trauma Surgery Podcasts in Credibility, Content, and Design

There has been an emerging trend of podcast use in medical education over the past decade. With the increasing number of podcasts and learners' interest in trauma surgery, it is vital to validate the quality of these podcasts. To determine the quantity and quality of podcast episodes on trauma surgery. This cross-sectional study identified trauma surgery podcasts published globally on the Google, Apple, and Spotify platforms as of May 31, 2023. An initial screening excluded podcasts not fulfilling the inclusion criteria (published as of May 31, 2023, and covering clinical knowledge or mentorship on general trauma surgery). A 10-item checklist developed from a modified Delphi consensus method was created to assess the podcasts' credibility, content, and design as indicators of quality. Data extracted from each podcast episode included the duration, setting, host and guest credentials, and fulfillment of quality indicators. Data were analyzed on August 13, 2023. The quality of each podcast was assessed using the 10-question checklist. A score of at least 80% in credibility (4 of 5 points), 75% in content (3 of 4 points), and 100% in design (1 of 1 point) was required to classify a podcast episode as having good quality. The initial search identified 204 podcasts on trauma surgery, of which 55 podcasts met the inclusion criteria. All 55 podcasts were published after 2014, with the majority hosted by males (85% [61 of 72 hosts]), based in the US (91% [50 of 55 podcasts]), and focused on clinical knowledge (80% [44 of 55 podcasts]). Podcast hosts and guests predominantly held a Doctor of Medicine degree (83% [113 of 136]), with 72% [46 of 64] of guests identifying as practicing trauma surgeons. While all 55 podcasts had excellent quality in terms of content and design, 20% (11 of 55) of podcasts were rated poorly on credibility. Credibility was further reduced for 60% (33 of 55) of podcasts because of nondisclosure of conflicts of interest. In this cross-sectional study of trauma surgery podcasts, most had good-quality content and design, but there was a lack of explicit conflict of interest disclosure. Content producers need to disclose their conflicts of interest appropriately to ensure credibility and improve the quality of their podcasts.

Competing discourses, contested roles: Electronic health records in medical education.

The integration of electronic health records (EHRs) into medical education remains contested despite their widespread use in clinical practice. For medical trainees, this has resulted in idiosyncratic and often ad hoc methods of instruction on EHR use. The purpose of this study was to understand the currently fragmented nature of EHR instruction by examining discourses of EHR use within the medical education literature. We conducted a Foucauldian critical discourse analysis to identify discourses of EHRs in the medical education literature. We found our texts through a systematic search of widely cited medical education journals from 2013-2023. Each text was analysed for recurring truth statements-claims framed as self-evidently true and thus not needing supporting evidence-about the role of EHRs in medical education. We identified three major discourses: (1) EHRs as a clinical skill and competency, emphasising training of physical interactions between learners, patients and computers; (2) EHRs as a system, emphasising the creation and facilitation of networks of people, technologies, institutions and standards; and (3) EHRs as a cognitive process, framed as a method to shape processes like clinical reasoning and bias. Each discourse privileged certain stakeholders over others and served to rationalise educational interventions that could be seen as beneficial in isolation yet were often disjointed in combination. Competing discourses of EHR use in medical education produce divergent interventions that exacerbate their contested role in contemporary medical education. Identifying different claims for the benefits of EHR use in these settings allows educators to make rational choices between competing educational directions.

Using ChatGPT in the Development of Clinical Reasoning Cases: A Qualitative Study.

Background There has been an explosion of commentary and discussion about the ethics and utility of using artificial intelligence in medicine, and its practical use in medical education is still being debated. Through qualitative research methods, this study aims to highlight the advantages and pitfalls of using ChatGPT in the development of clinical reasoning cases for medical student education. Methods Five highly experienced faculty in medical education were provided instructions to create unique clinical reasoning cases for three different chief concerns using ChatGPT 3.0. Faculty were then asked to reflect on and review the created cases. Finally, a focus group was conducted to further analyze and describe their experiences with the new technology. Results Overall, faculty found the use of ChatGPT in the development of clinical reasoning cases easy to use but difficult to get to certain objectives and largely incapable of being creative enough to create complexity for student use without heavy editing. The created cases did provide a helpful starting point and were extremely efficient; however, faculty did experience some medical inaccuracies and fact fabrication. Conclusion There is value to using ChatGPT to develop curricular content, especially for clinical reasoning cases, but it needs to be comprehensively reviewed and verified. To efficiently and effectively utilize the tool, educators will need to develop a framework that can be easily translatable into simple prompts that ChatGPT can understand. Future work will need to strongly consider the risks of recirculating biases and misinformation.

Effectiveness of Interprofessional Education Based on the Case-and Communication-Based Approach

Pharmacology role-playing, which follows a case and communication (C &amp; C)-based approach, has been developed for use in medical education, and its learning effects have been reported. To cultivate team spirit among medical professionals from an early stage, we proposed developing the C &amp; C approach for interprofessional education (IPE). Therefore, this study aimed to examine the learning effects of the C &amp; C approach during joint classes of medical and nursing students. In December 2017, we employed the C &amp; C approach to simulate a scenario in which drugs were explained to patients, and responses were collected from 103/115 medical (90%) and 55/60 nursing students (92%). The questionnaires used were designed to evaluate the following five items: understanding pharmacotherapy, understanding patients’ feelings, learning motivation, change in learning attitudes, and understanding IPE. Open-ended questions on learning content were analyzed based on free-text responses. Each student group acknowledged the effectiveness of the approach and the importance of cooperation. In total, 80.2% of medical students and 76.3% of nursing rated “understanding of IPE” as “very effective/effective.” Participants learned about the disparity in perspectives, with medical students gaining insight into nursing students’ approaches to patient interactions, and nursing students acquiring an understanding of doctors’ perspectives on disease and medical treatment. In conclusion, the C &amp; C approach proved beneficial as an IPE method, raising awareness among both nursing and medical students about the significance of communication skills and patient perspectives.

Pure Wisdom or Potemkin Villages? A Comparison of ChatGPT 3.5 and ChatGPT 4 on USMLE Step 3 Style Questions: Quantitative Analysis.

The United States Medical Licensing Examination (USMLE) has been critical in medical education since 1992, testing various aspects of a medical student's knowledge and skills through different steps, based on their training level. Artificial intelligence (AI) tools, including chatbots like ChatGPT, are emerging technologies with potential applications in medicine. However, comprehensive studies analyzing ChatGPT's performance on USMLE Step 3 in large-scale scenarios and comparing different versions of ChatGPT are limited. This paper aimed to analyze ChatGPT's performance on USMLE Step 3 practice test questions to better elucidate the strengths and weaknesses of AI use in medical education and deduce evidence-based strategies to counteract AI cheating. A total of 2069 USMLE Step 3 practice questions were extracted from the AMBOSS study platform. After including 229 image-based questions, a total of 1840 text-based questions were further categorized and entered into ChatGPT 3.5, while a subset of 229 questions were entered into ChatGPT 4. Responses were recorded, and the accuracy of ChatGPT answers as well as its performance in different test question categories and for different difficulty levels were compared between both versions. Overall, ChatGPT 4 demonstrated a statistically significant superior performance compared to ChatGPT 3.5, achieving an accuracy of 84.7% (194/229) and 56.9% (1047/1840), respectively. A noteworthy correlation was observed between the length of test questions and the performance of ChatGPT 3.5 (ρ=-0.069; P=.003), which was absent in ChatGPT 4 (P=.87). Additionally, the difficulty of test questions, as categorized by AMBOSS hammer ratings, showed a statistically significant correlation with performance for both ChatGPT versions, with ρ=-0.289 for ChatGPT 3.5 and ρ=-0.344 for ChatGPT 4. ChatGPT 4 surpassed ChatGPT 3.5 in all levels of test question difficulty, except for the 2 highest difficulty tiers (4 and 5 hammers), where statistical significance was not reached. In this study, ChatGPT 4 demonstrated remarkable proficiency in taking the USMLE Step 3, with an accuracy rate of 84.7% (194/229), outshining ChatGPT 3.5 with an accuracy rate of 56.9% (1047/1840). Although ChatGPT 4 performed exceptionally, it encountered difficulties in questions requiring the application of theoretical concepts, particularly in cardiology and neurology. These insights are pivotal for the development of examination strategies that are resilient to AI and underline the promising role of AI in the realm of medical education and diagnostics.

Can we use ChatGPT for Mental Health and Substance Use Education? Examining Its Quality and Potential Harms.

The use of generative artificial intelligence, more specifically large language models (LLMs), is proliferating, and as such, it is vital to consider both the value and potential harms of its use in medical education. Their efficiency in a variety of writing styles makes LLMs, such as ChatGPT, attractive for tailoring educational materials. However, this technology can feature biases and misinformation, which can be particularly harmful in medical education settings, such as mental health and substance use education. This viewpoint investigates if ChatGPT is sufficient for 2 common health education functions in the field of mental health and substance use: (1) answering users' direct queries and (2) aiding in the development of quality consumer educational health materials. This viewpoint includes a case study to provide insight into the accessibility, biases, and quality of ChatGPT's query responses and educational health materials. We aim to provide guidance for the general public and health educators wishing to utilize LLMs. We collected real world queries from 2 large-scale mental health and substance use portals and engineered a variety of prompts to use on GPT-4 Pro with the Bing BETA internet browsing plug-in. The outputs were evaluated with tools from the Sydney Health Literacy Lab to determine the accessibility, the adherence to Mindframe communication guidelines to identify biases, and author assessments on quality, including tailoring to audiences, duty of care disclaimers, and evidence-based internet references. GPT-4's outputs had good face validity, but upon detailed analysis were substandard in comparison to expert-developed materials. Without engineered prompting, the reading level, adherence to communication guidelines, and use of evidence-based websites were poor. Therefore, all outputs still required cautious human editing and oversight. GPT-4 is currently not reliable enough for direct-consumer queries, but educators and researchers can use it for creating educational materials with caution. Materials created with LLMs should disclose the use of generative artificial intelligence and be evaluated on their efficacy with the target audience.

Twelve tips for teaching in virtual reality

Virtual reality (VR) is a technology that is seeing increasing use in medical education as a means to complement or prepare students for clinical practice in a safe space. Whilst effective for learning, it can be difficult to use effectively and requires significant planning to avoid the technological tail wagging the educational dog. We have run educational sessions using the technology to teach anatomy and clinical reasoning that have been well received by students at Queen Mary, University of London. In this article, we share 12 practical tips from our experiences on how to create and deliver learning using VR.

Anterior Cruciate Ligament Stability in Soft-Embalmed Cadaver vs In vivo Knee: Alternative Approaches to Medical Education.

Soft-embalmed cadavers have been used in medical education with a variety of success in different curriculum objectives. In the United States, the ACL is the most commonly injured ligament. Yet, there has been little focus on the stability of the knee in the sagittal plane provided by the anterior cruciate ligament within the soft-embalmed cadaver model. If the soft-embalmed cadaver ligaments contain similar elastic properties as an in-vivo knees, this will offer yet another means for further advancements in medical education to detect and assess musculoskeletal injuries. Evaluate how similarly the anterior tibial translation of soft-embalmed cadaver anterior cruciate ligaments compares to in-vivo tissue. The KT-1000 arthrometer was used to assess the laxity of the anterior cruciate ligament of thirteen soft-embalmed cadavers consisting of five females and eight males with a mean age of 79.3 years and duration of time since embalming ranging from 250 to 1156 days. Anterior displacement of the tibia in relation to the femur was registered at 67N and 89N. The soft-embalmed cadaver measurements were compared against twenty-one healthy uninjured individuals whose anterior tibial translation was measured using the same process. Data sets were analyzed using a welch two-sample t-test to determine the similarity between the means of the data sets. The t-tests proved a significant difference between live and soft-embalmed cadaver knees. The anterior tibial translation in the set of healthy live knees directly compared to the soft-embalmed cadaver group for 67N depicts an average difference of 1.76mm. The same comparison at 89N depicts an average difference of 2.12mm. While soft-embalmed cadavers may not directly replicate ATT to an exact number to that of in vivo tissue, they still allow the perception of the tibial translation against a stationary femur. The difference is less than 2.5 mm in both data sets when compared to an in-vivo knee, equivalent to one-tenth of an inch. Suggesting the viability of soft-embalmed cadavers ATT and should not exclude their use in medical education.

Development of an anatomical breast phantom from polyvinyl chloride plastisol with lesions of various shape, elasticity and echogenicity for teaching ultrasound examination.

The WHO reported an increasing trend in the number of new cases of breast cancer, making it the most prevalent cancer in the world. This fact necessitates the availability of highly qualified ultrasonographers, which can be achieved by the widespread implementation of training phantoms. The goal of the present work is to develop and test an inexpensive, accessible, and reproducible technology for creating an anatomical breast phantom for practicing ultrasound diagnostic skills in grayscale and elastography imaging, as well as ultrasound-guided biopsy sampling. We used FDM 3D printer and PLA plastic for printing an anatomical breast mold. We made a phantom using a mixture of polyvinyl chloride plastisol, graphite powder, and metallic glitter to simulate soft tissues and lesions. Various degrees of elasticity were imparted using plastisols of stiffness ranging from 3 to 17 on the Shore scale. The lesions were shaped by hand. The materials and methods used are easily accessible and reproducible. Using the proposed technology, we have developed and tested a basic, differential, and elastographic versions of the breast phantom. The three versions of the phantom are anatomical and intended for use in medical education: the basic version is for practicing primary hand-eye coordination skills; the differential one is for practicing the differential diagnosis skills; the elastographic version helps developing the skills needed for assessing the stiffness of tissues. The proposed technology allows the creation of breast phantoms for practicing hand-eye coordination and develop the critical skills for navigation and assessment of the shape, margins, and size of the lesion, as well as performing an ultrasound-guided biopsy. It is cost-effective, reproducible, and easily implementable, and could be instrumental in generating ultrasonographers with crucial skills for accurate diagnosis of breast cancer, especially in low-resource settings.

Improving the Teaching of Histology by Using the Manual Whole Slide Imaging Technology

The tissue sample analysis based on the visualization of the entire tissue sample represents an important methodology in clinical diagnostics, research, and education. This process that enables visualization of the entire tissue sample is named whole slide imaging. For clinical purposes, professional and very expensive scanners are used for tissue sample digitization and the creation of whole slide images. In education, it is possible to use whole slide images that do not necessarily follow strict medical standards and regulations. Therefore, the important research task is to propose a methodology for the manual creation of whole slide images and its adequate use in medical education. In this paper, the use of manually created whole slide images in histology classes education was analyzed. In the experimental part of this research, the methodology of manual creation and usage of whole slide images (WSI) in histology education is presented in more detail. The subjective assessment of the user's satisfaction with WSI usage in education was also performed. It was shown that the whole slide images were evaluated very positively in the histological analysis and education process. Using WSI can significantly improve histology teaching, especially when distance education principles are implemented. Therefore, in crises like the pandemic situation COVID-19 WSI usage becomes a very important educational tool for teaching microscope-related topics. The results of this research showed that the use of whole slide images in histology classes is in line with the process of digital transformation in education.

Silicone Models for Dermatological Education: Assessment of a New Teaching Tool by Dermatologists.

The coronavirus pandemic forced universities to transfer academic curricula into the digital realm and calls for the introduction of new teaching methods to adequately compensate for the limited in-patient training. Especially in the field of dermatology, the use of 3D models presents an interesting opportunity to maintain the teaching of diagnostically essential sensory and haptic characteristics of primary lesions. We developed a prototype silicone model and presented it to the medical service of the Department of Dermatology of the Ludwig-Maximilians University for evaluation. Silicone models demonstrating primary skin lesions were produced by using negative 3D-printed molds and different types of silicone. An online survey obtained evaluations from a group of dermatologists regarding the quality of previously supplied silicone 3D models and their potential use in medical education. Data from 58 dermatologists were collected and analyzed. The majority of the participants rated the models overall as positive and innovative, providing constructive feedback for additional modifications, and recommended further implementation into the regular curriculum as an additional tool after the end of the pandemic. Our study underlined the possible advantages of using 3D models as a supplement in educational training even after the end of the SARS-CoV-2 pandemic.

Using Podcasts to Study Efficiently and Increase Exposure to Medical Specialties.

To the Editor: As medical students and avid podcast listeners, we read with great interest Kelly and colleagues’ study, which sought to improve understanding of podcast use in medical education. 1 To speak to the utility of podcasts as accessible and flexible learning tools, below we present empirical observations related to medical student podcast usage. We agree with Kelly and colleagues that a significant benefit of medical education podcasts is their portability and efficiency. 1 Students often have unproductive periods of time, such as commuting, where traditional study methods may be impractical. Using podcasts to study whenever possible can increase efficiency and free time, leading to an improved work–life balance and decreased stress levels. At our respective institutions, podcasts were incorporated into our first-year curriculum. Our experiences support Kelly and colleagues’ finding that medical education podcasts provide flexible, learner-specific, and entertaining content. 1 Listening to information-dense podcasts, rather than attending in-person lectures, allows us to maximize our time. Because podcasts split information into digestible portions, we find it easier to focus compared with traditional didactic lectures. We observe that many of our peers also value the ability to pause and customize the playback speed of podcasts, allowing for personalized learning. In addition to functioning as learning tools, podcasts offer exposure to specialties that are less encountered in medical education. For example, The Undifferentiated Medical Student (https://www.undifferentiatedmedicalstudent.com) has helped us learn about specialties that are not well represented at our institutions. Podcasts like this one are especially pertinent for medical students who may not experience a robust and diverse clinical curriculum. We thank Kelly and colleagues for sharing their insights and encourage future studies to similarly assess podcasts featuring medical specialty information. Learning about medical specialties is as important as learning about medical practice. For specialties that are facing shortages of physicians or little exposure from medical students, creating conversations about their scope and culture is necessary to attract potential applicants. We believe that podcasts can help bridge this gap.