Artificial Intelligence In Medical Education Research Articles

Harnessing Generative Artificial Intelligence for Medical Education.

Harnessing Generative Artificial Intelligence for Medical Education.

Synergy and collaboration with young educators and students: Insights from an open forum on generative artificial intelligence in medical education.

Synergy and collaboration with young educators and students: Insights from an open forum on generative artificial intelligence in medical education.

GPT-4/4V's performance on the Japanese National Medical Licensing Examination

Background Recent advances in Artificial Intelligence (AI) are changing the medical world, and AI will likely replace many of the actions performed by medical professionals. The overall clinical ability of the AI has been evaluated by its ability to answer a text-based national medical examination. This study uniquely assesses the performance of Open AI's ChatGPT against all Japanese National Medical Licensing Examination (NMLE), including images, illustrations, and pictures. Methods We obtained the questions of the past six years of the NMLE (112th to 117th) from the Japanese Ministry of Health, Labour and Welfare website. We converted them to JavaScript Object Notation (JSON) format. We created an application programming interface (API) to output correct answers using GPT-4 for questions without images and GPT4-V(ision) or GPT4 console for questions with images. Results The percentage of image questions was 723/2400 (30.1%) over the past six years. In all years, GPT-4/4V exceeded the minimum score the examinee should score. In total, over the six years, the percentage of correct answers for basic medical knowledge questions was 665/905 (73.5%); for clinical knowledge questions, 1143/1531 (74.7%); and for image questions 497/723 (68.7%), respectively. Conclusions Regarding medical knowledge, GPT-4/4V met the minimum criteria regardless of whether the questions included images, illustrations, and pictures. Our study sheds light on the potential utility of AI in medical education.

The Role of Artificial Intelligence in Medical Education: A Systematic Review.

To examine the artificial intelligence (AI) tools currently being studied in modern medical education, and critically evaluate the level of validation and the quality of evidence presented in each individual study. This review (PROSPERO ID: CRD42023410752) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. A database search was conducted using PubMed, Embase, and Cochrane Library. Articles written in the English language between 2000 and March 2023 were reviewed retrospectively using the MeSH Terms "AI" and "medical education" A total of 4642 potentially relevant studies were found. After a thorough screening process, 36 studies were included in the final analysis. These studies consisted of 26 quantitative studies and 10 studies investigated the development and validation of AI tools. When examining the results of studies in which Support vector machines (SVMs) were employed, it has demonstrated high accuracy in assessing students' experiences, diagnosing acute abdominal pain, classifying skilled and novice participants, and evaluating surgical training levels. Particularly in the comparison of surgical skill levels, it has achieved an accuracy rate of over 92%. AI tools demonstrated effectiveness in improving practical skills, diagnosing diseases, and evaluating student performance. However, further research with rigorous validation is required to identify the most effective AI tools for medical education.

Application of artificial intelligence-assisted image diagnosis software based on volume data reconstruction technique in medical imaging practice teaching

BackgroundIn medical imaging courses, due to the complexity of anatomical relationships, limited number of practical course hours and instructors, how to improve the teaching quality of practical skills and self-directed learning ability has always been a challenge for higher medical education. Artificial intelligence-assisted diagnostic (AISD) software based on volume data reconstruction (VDR) technique is gradually entering radiology. It converts two-dimensional images into three-dimensional images, and AI can assist in image diagnosis. However, the application of artificial intelligence in medical education is still in its early stages. The purpose of this study is to explore the application value of AISD software based on VDR technique in medical imaging practical teaching, and to provide a basis for improving medical imaging practical teaching.MethodsTotally 41 students majoring in clinical medicine in 2017 were enrolled as the experiment group. AISD software based on VDR was used in practical teaching of medical imaging to display 3D images and mark lesions with AISD. Then annotations were provided and diagnostic suggestions were given. Also 43 students majoring in clinical medicine from 2016 were chosen as the control group, who were taught with the conventional film and multimedia teaching methods. The exam results and evaluation scales were compared statistically between groups.ResultsThe total skill scores of the test group were significantly higher compared with the control group (84.51 ± 3.81 vs. 80.67 ± 5.43). The scores of computed tomography (CT) diagnosis (49.93 ± 3.59 vs. 46.60 ± 4.89) and magnetic resonance (MR) diagnosis (17.41 ± 1.00 vs. 16.93 ± 1.14) of the experiment group were both significantly higher. The scores of academic self-efficacy (82.17 ± 4.67) and self-directed learning ability (235.56 ± 13.50) of the group were significantly higher compared with the control group (78.93 ± 6.29, 226.35 ± 13.90).ConclusionsApplying AISD software based on VDR to medical imaging practice teaching can enable students to timely obtain AI annotated lesion information and 3D images, which may help improve their image reading skills and enhance their academic self-efficacy and self-directed learning abilities.

Perceptions of Faculty and Students About Use of Artificial Intelligence in Medical Education: A Qualitative Study.

Artificial intelligence (AI) implies using a computer to model intelligent behavior with minimal human intervention. With the advances of AI use in healthcare comes the need to reform medical education to produce doctors competentin AI use. Therefore, this qualitative study was conducted to explore faculty and students' perspectives on AI, their use of AI applications, and their perspective on its value and impact on medical education at a Saudi faculty of medicine. This qualitative study was conducted at the Faculty of Medicine, Jazan University in Saudi Arabia. A direct interview was held with 11 faculty members, and six focus group discussions were conducted with students from the second to sixth year (34 students). Data were collected using semi-structured open-ended interview questions based on relevant literature. Most respondents (91.11%) believed AI systems would positively impact medical education, especially in research, knowledge gain, assessment, and simulation. However, ethical concerns were raised about threats to academic integrity, plagiarism, privacy/confidentiality issues, and AI's lacking cultural sensitivity. Faculty and students felt a need for training on AI use (80%) and that the curriculum could adapt to integrate AI (64.44%), though resources were seen as currently needing to be improved. AI's potential to enhance medical education is generally viewed positively in the study, but ethical concerns must be addressed. Integrating AI into medical education programs requires adequate resources, training, and curriculum adaptation. There is still a need for further research in this area to develop comprehensive strategies.

Incorporating artificial intelligence into a workshop on scientific and scholarly report writing for preclinical medical students

This article presents the use of an artificial intelligence (AI) tool (ChatGPT) to transform a didactic lecture to an interactive workshop. The workshop on scientific and scholarly report writing was conducted for preclinical medical students. The functions of the AI as teaching assistance and documentation were expected to support the workshop. In this workshop, the AI was used to summarise and generate the summary notes based on students’ inputs throughout the interactive activities during the workshop. Future research should focus on potential functions of AI in medical education and its impacts on student’s learning outcomes.

Application of artificial intelligence in medical education: A review of benefits, challenges, and solutions

Introduction/objectivesArtificial intelligence (AI) holds indisputable potential in solving global challenges faced in healthcare provision, resulting in its ever-increasing utilization in various medical fields. This review aims to present a thorough evaluation of the advantages, challenges, and leading strategies for utilizing AI in the field of medical education. MethodsIn order to access the latest studies and documents, a comprehensive literature search was conducted in Medline (via PubMed), Scopus, and Web of Science databases, using free keywords and MeSH terms representing artificial intelligence, medical education, curriculum, medical student, and their equivalents, without any restriction in time and language, until October 2023. ResultsThe expanding integration of AI into medical practice, especially in recent decades, has also led to an increase in the use of different AI methods in medical education. However, integrating AI into medical education comes with advantages and obstacles. The advantages include objective student assessment, better clinical simulation organization, and enhanced education transparency. Conversely, the main challenges of AI integration in education are ethical and legal issues, scalability limitations, evaluating the effectiveness of these educational methods, and technical difficulties. ConclusionsFurther research is needed to specifically identify the potential benefits and challenges, and analyze the proposed solutions for these challenges. Also, the academic curriculum should be evaluated periodically in order to improve the effectiveness of incorporating AI in medical education.

Artificial Intelligence in Medicine: Cross-Sectional Study Among Medical Students on Application, Education, and Ethical Aspects.

The use of artificial intelligence (AI) in medicine not only directly impacts the medical profession but is also increasingly associated with various potential ethical aspects. In addition, the expanding use of AI and AI-based applications such as ChatGPT demands a corresponding shift in medical education to adequately prepare future practitioners for the effective use of these tools and address the associated ethical challenges they present. This study aims to explore how medical students from Germany, Austria, and Switzerland perceive the use of AI in medicine and the teaching of AI and AI ethics in medical education in accordance with their use of AI-based chat applications, such as ChatGPT. This cross-sectional study, conducted from June 15 to July 15, 2023, surveyed medical students across Germany, Austria, and Switzerland using a web-based survey. This study aimed to assess students' perceptions of AI in medicine and the integration of AI and AI ethics into medical education. The survey, which included 53 items across 6 sections, was developed and pretested. Data analysis used descriptive statistics (median, mode, IQR, total number, and percentages) and either the chi-square or Mann-Whitney U tests, as appropriate. Surveying 487 medical students across Germany, Austria, and Switzerland revealed limited formal education on AI or AI ethics within medical curricula, although 38.8% (189/487) had prior experience with AI-based chat applications, such as ChatGPT. Despite varied prior exposures, 71.7% (349/487) anticipated a positive impact of AI on medicine. There was widespread consensus (385/487, 74.9%) on the need for AI and AI ethics instruction in medical education, although the current offerings were deemed inadequate. Regarding the AI ethics education content, all proposed topics were rated as highly relevant. This study revealed a pronounced discrepancy between the use of AI-based (chat) applications, such as ChatGPT, among medical students in Germany, Austria, and Switzerland and the teaching of AI in medical education. To adequately prepare future medical professionals, there is an urgent need to integrate the teaching of AI and AI ethics into the medical curricula.

Effective Integration of Artificial Intelligence in Medical Education: Practical Tips and Actionable Insights

Effective Integration of Artificial Intelligence in Medical Education: Practical Tips and Actionable Insights

Integration of Artificial Intelligence in Medical Education: Opportunities, Challenges, and Ethical Considerations

Integration of Artificial Intelligence in Medical Education: Opportunities, Challenges, and Ethical Considerations

Educators Who Do Not Use AI Will Be Replaced by Those Who Do: Disadvantages of Not Embracing AI in Medical Education

Resistance among educators to using artificial intelligence (AI) may yield significant disadvantages. Without AI, educators risk a higher workload, inefficient use of time, limited personalization, lack of feedback, inability to adapt, reduced student engagement, fewer job opportunities, and limited professional development. However, AI is designed to empower educators, enhance instructional practices, and foster professional growth. In the field of medicine, reluctance to adopt AI could hinder students from being equipped with vital skills in tackling the modern health-care situation. By leveraging AI tools, educators can create dynamic learning environments, producing better-trained health-care professionals ready to tackle evolving challenges. AI integration enhances education quality and prepares students for technological advancements in their medical careers, ensuring they are equipped to navigate the complexities of modern health-care practice. Thus, AI adoption in education is essential for maintaining the competitiveness and relevance of curricula in the AI age.

Medical Student Experiences and Perceptions of ChatGPT and Artificial Intelligence: Cross-Sectional Study.

Artificial intelligence (AI) has the potential to revolutionize the way medicine is learned, taught, and practiced, and medical education must prepare learners for these inevitable changes. Academic medicine has, however, been slow to embrace recent AI advances. Since its launch in November 2022, ChatGPT has emerged as a fast and user-friendly large language model that can assist health care professionals, medical educators, students, trainees, and patients. While many studies focus on the technology's capabilities, potential, and risks, there is a gap in studying the perspective of end users. The aim of this study was to gauge the experiences and perspectives of graduating medical students on ChatGPT and AI in their training and future careers. A cross-sectional web-based survey of recently graduated medical students was conducted in an international academic medical center between May 5, 2023, and June 13, 2023. Descriptive statistics were used to tabulate variable frequencies. Of 325 applicants to the residency programs, 265 completed the survey (an 81.5% response rate). The vast majority of respondents denied using ChatGPT in medical school, with 20.4% (n=54) using it to help complete written assessments and only 9.4% using the technology in their clinical work (n=25). More students planned to use it during residency, primarily for exploring new medical topics and research (n=168, 63.4%) and exam preparation (n=151, 57%). Male students were significantly more likely to believe that AI will improve diagnostic accuracy (n=47, 51.7% vs n=69, 39.7%; P=.001), reduce medical error (n=53, 58.2% vs n=71, 40.8%; P=.002), and improve patient care (n=60, 65.9% vs n=95, 54.6%; P=.007). Previous experience with AI was significantly associated with positive AI perception in terms of improving patient care, decreasing medical errors and misdiagnoses, and increasing the accuracy of diagnoses (P=.001, P<.001, P=.008, respectively). The surveyed medical students had minimal formal and informal experience with AI tools and limited perceptions of the potential uses of AI in health care but had overall positive views of ChatGPT and AI and were optimistic about the future of AI in medical education and health care. Structured curricula and formal policies and guidelines are needed to adequately prepare medical learners for the forthcoming integration of AI in medicine.

Generative artificial intelligence in medical education: way to solve the problems.

Generative artificial intelligence in medical education: way to solve the problems.

Relevance of the use of artificial intelligence in modern conditions of the educational process

The implementation of artificial intelligence in education, especially within the field of medicine, is of paramount significance, and this article explores the transformative impact of artificial intelligence on the educational process and the future of medical professionals. This analysis delves into how artificial intelligence technology is revolutionizing learning methods and aiding aspiring doctors in acquiring new skills and knowledge. The article commences with an overview of the current challenges facing medical education, highlighting the pressing need for curriculum updates and innovative teaching methods. These challenges include keeping pace with the rapid advancements in the medical field, ensuring students are adequately prepared for real-world clinical scenarios, and providing access to a diverse range of educational resources. One of the key areas where artificial intelligence plays a crucial role is in providing virtual laboratories and simulation models. These technologies offer a safe and controlled environment for medical students to practice procedures and explore complex clinical situations. Through artificial intelligence-driven simulations, students can refine their diagnostic and problem-solving skills, all while minimizing risks associated with live patient interactions. Personalized learning is another vital component of artificial intelligence in medical education. artificial intelligence algorithms analyze the progress and learning preferences of each student, tailoring the educational content to their individual needs. Moreover, the authors discuss the potential for collaboration between artificial intelligence systems and human medical educators. This partnership can leverage the strengths of both artificial intelligence-driven technologies and the expertise of experienced teachers. In this way, students can benefit from the best of both worlds, ensuring a well-rounded education that combines the efficiency of artificial intelligence with the wisdom and guidance of skilled instructors.

Opportunities, Challenges, and Future Directions of Generative Artificial Intelligence in Medical Education: Scoping Review.

Generative artificial intelligence (AI) technologies are increasingly being utilized across various fields, with considerable interest and concern regarding their potential application in medical education. These technologies, such as Chat GPT and Bard, can generate new content and have a wide range of possible applications. This study aimed to synthesize the potential opportunities and limitations of generative AI in medical education. It sought to identify prevalent themes within recent literature regarding potential applications and challenges of generative AI in medical education and use these to guide future areas for exploration. We conducted a scoping review, following the framework by Arksey and O'Malley, of English language articles published from 2022 onward that discussed generative AI in the context of medical education. A literature search was performed using PubMed, Web of Science, and Google Scholar databases. We screened articles for inclusion, extracted data from relevant studies, and completed a quantitative and qualitative synthesis of the data. Thematic analysis revealed diverse potential applications for generative AI in medical education, including self-directed learning, simulation scenarios, and writing assistance. However, the literature also highlighted significant challenges, such as issues with academic integrity, data accuracy, and potential detriments to learning. Based on these themes and the current state of the literature, we propose the following 3 key areas for investigation: developing learners' skills to evaluate AI critically, rethinking assessment methodology, and studying human-AI interactions. The integration of generative AI in medical education presents exciting opportunities, alongside considerable challenges. There is a need to develop new skills and competencies related to AI as well as thoughtful, nuanced approaches to examine the growing use of generative AI in medical education.

Artificial Intelligence in Medical Education: Comparative Analysis of ChatGPT, Bing, and Medical Students in Germany.

Large language models (LLMs) have demonstrated significant potential in diverse domains, including medicine. Nonetheless, there is a scarcity of studies examining their performance in medical examinations, especially those conducted in languages other than English, and in direct comparison with medical students. Analyzing the performance of LLMs in state medical examinations can provide insights into their capabilities and limitations and evaluate their potential role in medical education and examination preparation. This study aimed to assess and compare the performance of 3 LLMs, GPT-4, Bing, and GPT-3.5-Turbo, in the German Medical State Examinations of 2022 and to evaluate their performance relative to that of medical students. The LLMs were assessed on a total of 630 questions from the spring and fall German Medical State Examinations of 2022. The performance was evaluated with and without media-related questions. Statistical analyses included 1-way ANOVA and independent samples t tests for pairwise comparisons. The relative strength of the LLMs in comparison with that of the students was also evaluated. GPT-4 achieved the highest overall performance, correctly answering 88.1% of questions, closely followed by Bing (86.0%) and GPT-3.5-Turbo (65.7%). The students had an average correct answer rate of 74.6%. Both GPT-4 and Bing significantly outperformed the students in both examinations. When media questions were excluded, Bing achieved the highest performance of 90.7%, closely followed by GPT-4 (90.4%), while GPT-3.5-Turbo lagged (68.2%). There was a significant decline in the performance of GPT-4 and Bing in the fall 2022 examination, which was attributed to a higher proportion of media-related questions and a potential increase in question difficulty. LLMs, particularly GPT-4 and Bing, demonstrate potential as valuable tools in medical education and for pretesting examination questions. Their high performance, even relative to that of medical students, indicates promising avenues for further development and integration into the educational and clinical landscape.

Application of Artificial Intelligence in Medical Education: Current Scenario and Future Perspectives.

Medical education is a lifetime learning process stretching from undergraduate to postgraduate, specialty training, and beyond. It also applies to various healthcare professionals, including doctors, nurses, and other allied healthcare professionals. Therefore, it is essential to acknowledge the immense role of artificial intelligence in medical education in the current era of rapidly growing technology. High-quality data that met the study objectives were included. In addition, comprehensive investigations on articles available in reputable databases such as PubMed, Research Gate, PubMed central, Web of Science, and Google Scholar were considered for literature review. Artificial intelligence has fixed various issues in education during the last decade, including language processing, reasoning, planning, and cognitive modelling. It can be used in medical education in the following forms: Virtual Inquiry System, Medical Distance Learning and Management, and Recording teaching videos in medical schools. It can also enhance the value of the non-analytical humanistic aspects of medicine. The goal of this review article was to present the implications of AI in medical education, now and in the coming years.

Exploring the Hurdles in Integrating Artificial Intelligence in Medical Education: A Talk with ChatGPT

The integration of artificial intelligence (AI) in the medical field is rapidly increasing, and its importance is recognised as it can analyse medical information and improve patient outcomes. The Chat Generative Pre-trained Transformer (ChatGPT) by OpenAI is a language model that can respond to prompts in a human-like manner and has the potential to transform medical education. In this conversation, the authors engage with ChatGPT to discuss the challenges associated with the application of AI in medical education. ChatGPT acknowledges the limitations of AI in medical education and the importance of students demonstrating their understanding during assessments. It also discusses the need for academic policies to ensure the responsible and ethical use of AI in medical education.

Application of artificial intelligence in medical education: focus on the application of ChatGPT for clinical medical education

This study explores the potential use of artificial intelligence (AI)-based services, specifically ChatGPT-3.5, in medical education. The application of this technology is acknowledged as a valuable tool for simulating authentic clinical scenarios and enhancing learners' diagnostic and communication skills. To construct a case, students received ChatGPT training using a clinical ethics casebook titled "Clinical Ethics Cases and Commentaries for Medical Students and Physicians." Subsequently, a role-play script was generated based on this training. The initial draft of the script was reviewed by two medical professors and was further optimized using ChatGPT-3.5. Consequently, a comprehensive role-play script, accurately reflecting real-world clinical situations, was successfully developed. This study demonstrates the potential for effectively integrating AI technology into medical education and provides a solution to overcome limitations in developing role-play scripts within conventional educational settings. However, the study acknowledges that AI cannot always generate flawless role-play scripts and recognizes the necessity of addressing these limitations and ethical concerns. The research explores both the potential and limitations of employing AI in the early stages of medical education, suggesting that future studies should focus on overcoming these limitations while further investigating the potential applications of AI in this field.