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Komasawa, N., & Yokohira, M. (2023). Learner-Centered Experience-Based Medical Education in an AI-Driven Society : A Literature Review.

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Komasawa, N., & Yokohira, M. (2023). Learner-Centered Experience-Based Medical Education in an AI-Driven Society : A Literature Review.

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Summary: This literature review by Komasawa and Yokohira (2023) advocates for learner-centred experience-based medical education (EXPBME) in the age of artificial intelligence (AI). The authors highlight the transformative potential of third-generation AI in healthcare, but also emphasise the crucial role of non-technical skills—particularly in handling AI's inherent fallibilities—that can only be effectively developed through EXPBME in clinical or simulated settings. The review argues that integrating AI literacy with EXPBME, alongside career development modules, is paramount for adequately preparing future healthcare professionals to navigate the complexities of an AI-driven healthcare system, thereby ensuring both patient safety and optimal care. The study concludes that while AI rapidly evolves, the core principles of experiential learning remain essential in medical education.

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Source Image : AdobeStock_576595621 - Licence Education

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Veille sur l'AI dans l'enseignement supérieur

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Veille sur l'AI dans l'enseignement supérieur

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Le service Didanum de l’université de Fribourg (https://www.unifr.ch/didanum/fr/) vous propose une veille techno-pédagogique sur le développement des usages de l’AI dans l’enseignement de différentes disciplines universitaires.

Chaque fiche issue de cette activité de veille analyse les contenus d’une publication scientifique. Pour ce faire, un outil d'intelligence artificielle a facilité l'automatisation de certaines tâches préliminaires de notre veille technologique. Ensuite, chaque article a été rigoureusement lu et l'analyse validée par un-e membre de notre équipe, garantissant ainsi la fiabilité et la pertinence de nos fiches.

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A watch about AI uses in university teaching

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A watch about AI uses in university teaching

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The Didanum service at the University of Fribourg (https://www.unifr.ch/didanum/fr/ ) offers you a techno-pedagogical watch on the development of the use of AI in the teaching of various university disciplines.

Each sheet produced by this monitoring activity analyses the content of a scientific publication. To do this, an artificial intelligence tool was used for certain preliminary tasks in our watch. Each article was then rigorously read and the analysis validated by a member of our team, guaranteeing the reliability and relevance of our data sheets.

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Ce padlet est dédié à l'enseignement de la médecine 

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Ce padlet est dédié à l'enseignement de la médecine 

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D’Souza, R. F. et al. (2024). Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education. 

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D’Souza, R. F. et al. (2024). Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education. 

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Summary: This article by D’Souza et al. (2024) offers twelve practical tips for ethically integrating artificial intelligence (AI) into medical education. The authors highlight crucial ethical concerns, such as bias in algorithms, data privacy, and transparency in AI decision-making. Their proposed tips cover a range of strategies, from obtaining informed consent and fostering collaboration between stakeholders to establishing accountability and forming an ethics committee. Ultimately, the article aims to guide medical educators towards the responsible and sustainable use of AI in medical education, ensuring both technological advancement and ethical integrity.

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Gordon, M. et al. (2024). A scoping review of artificial intelligence in medical education : BEME Guide No. 84

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Gordon, M. et al. (2024). A scoping review of artificial intelligence in medical education : BEME Guide No. 84

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NB: For this article, the result of the automatic process by our Chatbot is not good. (Hypothesis: The structure of the article is the reason.)

Summary: This 2024 article from Medical Teacher presents a scoping review of artificial intelligence (AI) applications in medical education, guided by BEME and STORIES guidelines. The authors systematically reviewed 278 publications across various databases, charting the use of AI in admissions, teaching, assessment, and clinical reasoning, among other areas. Key findings highlight AI's diverse roles – from supplementing existing methods to creating entirely new ones – while underscoring the urgent need for ethical guidelines. The review concludes by proposing the FACETS framework for future high-quality reporting on AI in medical education and identifying areas ripe for further systematic investigation.

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Guimaraes, B. et al. (2017). Rethinking Anatomy : How to Overcome Challenges of Medical Education’s Evolution.

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Guimaraes, B. et al. (2017). Rethinking Anatomy : How to Overcome Challenges of Medical Education’s Evolution.

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Summary: This 2017 review article by Guimarães et al. examines the evolving challenges and pedagogical responses within anatomy education in medical schools. The authors highlight a shrinking curriculum time dedicated to anatomy, coupled with the rise of medical imaging and minimally invasive procedures, prompting a shift away from traditional cadaveric dissection. They advocate for a "blended learning" approach, integrating traditional methods with technology-based solutions like 3D models, virtual reality, and computer-assisted learning and assessment. The ultimate goal is to improve learning outcomes through a more student-centred, personalised, and data-driven approach incorporating Learning Analytics and Artificial Intelligence.

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Han, E. et al. (2019). Medical education trends for future physicians in the era of advanced technology and artificial intelligence : An integrative review.

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Han, E. et al. (2019). Medical education trends for future physicians in the era of advanced technology and artificial intelligence : An integrative review.

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Summary: This 2019 integrative review by Han et al. examines trends in undergraduate medical education, focusing on preparing future physicians for a rapidly evolving healthcare landscape dominated by advanced technology and artificial intelligence. The authors synthesise findings from 28 studies to identify four key themes: a humanistic approach to patient safety, achieved through fostering empathy and interprofessional collaboration; early clinical experience and longitudinal integration to enhance learning and patient-centred care; extending medical education beyond hospitals into the community to address diverse societal needs; and finally, leveraging student-driven learning with advanced technology for individualised learning and resource accessibility. The study's purpose is to inform medical educators on curriculum development, highlighting successful educational programs while acknowledging the need for further research into graduate and continuing medical education.

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Preiksaitis, C., & al. (2023). Opportunities, Challenges, and Future Directions of Generative Artificial Intelligence in Medical Education : Scoping Review.

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Preiksaitis, C., & al. (2023). Opportunities, Challenges, and Future Directions of Generative Artificial Intelligence in Medical Education : Scoping Review.

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Summary: This preprint scoping review by Preiksaitis and Rose explores the opportunities and challenges of using generative AI in medical education. The authors systematically reviewed recent literature to identify prevalent themes, revealing potential benefits like personalised learning and writing assistance, and significant limitations, such as concerns about academic integrity and data accuracy. The review proposes three key areas for future investigation: developing AI literacy among learners, rethinking assessment methods, and studying human-AI interactions. Ultimately, the article aims to provide medical educators with a comprehensive overview to guide the thoughtful and ethical integration of AI in health professions education.

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Lee, J. et al. (2021). Artificial Intelligence in Undergraduate Medical Education : A Scoping Review.

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Lee, J. et al. (2021). Artificial Intelligence in Undergraduate Medical Education : A Scoping Review.

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Summary: This 2021 scoping review by Lee et al. examines the integration of artificial intelligence (AI) into undergraduate medical education (UME). The authors analysed 22 studies to identify key themes and gaps in the existing literature, revealing a significant lack of consensus on what AI-related content should be taught and how it should be delivered. Key themes identified include the need for AI literacy, ethical considerations, and the cultivation of uniquely human skills alongside AI training. The review concludes that a standardised framework of competencies and evidence-based curriculum development, along with greater publication of findings, are urgently needed to guide effective AI integration in UME.

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Lillehaug, S., & Lajoie, S. (1998). AI in medical education—Another grand challenge for medical informatics.

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Lillehaug, S., & Lajoie, S. (1998). AI in medical education—Another grand challenge for medical informatics.

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too old?

Summary: This 1998 paper by Lillehaug and Lajoie critiques the underachievement of Artificial Intelligence in Medicine (AIM), arguing that its focus on automated diagnosis has neglected the crucial role of empowering healthcare workers. The authors propose a shift towards Artificial Intelligence in Medical Education (AIME), advocating for intelligent learning environments (ILEs) that leverage cognitive science and AI to enhance learning and problem-solving skills. They support this shift by reviewing research demonstrating the strong link between healthcare workers' knowledge, job satisfaction, and the quality of care, asserting that empowering healthcare professionals leads to better patient outcomes. The paper showcases successful ILEs from other fields and presents examples of promising AIME projects in medicine, concluding that this approach offers a more impactful and sustainable path to improving healthcare than solely focusing on automated decision-support systems.

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Zhang, Y. et al. (2024). Exploring the Application of the Artificial-Intelligence-Integrated Platform 3D Slicer in Medical Imaging Education

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Zhang, Y. et al. (2024). Exploring the Application of the Artificial-Intelligence-Integrated Platform 3D Slicer in Medical Imaging Education

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Summary: This research article, "Exploring the Application of the Artificial-Intelligence-Integrated Platform 3D Slicer in Medical Imaging Education," investigates the potential of 3D Slicer, an open-source medical image analysis platform, as a valuable tool in medical education. The study highlights the growing need for AI proficiency in medical imaging professionals due to advancements in AI technologies like ChatGPT. The authors conduct a literature review to determine the applications of 3D Slicer in clinical medical imaging and explore its potential use in education, whilst also acknowledging limitations that could hinder its inclusion in medical imaging education. The central argument is that 3D Slicer, with its capacity for image segmentation, reconstruction, and quantitative analysis, can enhance medical imaging education by fostering practical skills and independent learning for students.

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Narayanan, S. et al. (2023). Artificial Intelligence Revolutionizing the Field of Medical Education.

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Narayanan, S. et al. (2023). Artificial Intelligence Revolutionizing the Field of Medical Education.

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Summary: This paper by Narayanan et al. explores the transformative potential of artificial intelligence (AI) in medical education. It examines how AI can be used to enhance teaching and learning, particularly through tools like chatbots, intelligent tutoring systems, and virtual patients, and how these tools can aid in adaptive and gamified learning experiences. Furthermore, the review investigates AI's role in assessment, discussing methods such as automated essay scoring, VR-based procedural skill evaluation and emergency response evaluation. Finally, the paper also considers the use of AI for administrative tasks and research within medical institutions, addressing challenges and offering a balanced perspective on the future of AI in medical education.

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Xu, X. et al. (2024). Opportunities, challenges, and future directions of large language models, including ChatGPT in medical education : A systematic scoping review.

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Xu, X. et al. (2024). Opportunities, challenges, and future directions of large language models, including ChatGPT in medical education : A systematic scoping review.

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Summary : This paper is a systematic scoping review exploring the opportunities, challenges, and future directions of using large language models like ChatGPT in medical education. It highlights that ChatGPT can be used to personalise learning, simulate clinical scenarios, and assist with writing, but also identifies concerns around academic integrity, data accuracy, and potential harm to learning. The study underscores the need for guidelines and training to ensure responsible and effective integration of AI in medical education, and proposes further research into cultivating correct usage, integrating ChatGPT into teaching, and establishing AI usage standards for medical students. Ultimately, the review concludes that ChatGPT has transformative potential, but its impact on both students and teachers must be carefully considered.

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Weidener, L., & al. (2023). Teaching AI Ethics in Medical Education : A Scoping Review of Current Literature and Practices.

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Weidener, L., & al. (2023). Teaching AI Ethics in Medical Education : A Scoping Review of Current Literature and Practices.

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Summary: This academic paper presents a scoping review examining the current state of research and practices in teaching AI ethics within medical education. The authors conducted a systematic search of literature up to June 2023 to understand how this crucial topic is being addressed in medical curricula. Their findings reveal a limited amount of existing literature, which is primarily recent and theoretical, highlighting a significant gap. Consequently, the paper argues for the urgent need for more empirical studies and a foundational definition of AI ethics to better equip future doctors for the ethical challenges posed by the increasing integration of artificial intelligence in healthcare.

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Walsh, G., & al. (2023). Responsible AI practice and AI education are central to AI implementation : A rapid review for all medical imaging professionals in Europe.

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Walsh, G., & al. (2023). Responsible AI practice and AI education are central to AI implementation : A rapid review for all medical imaging professionals in Europe.

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Article not dealing with generative AI

Summary: This 2023 review article, "Responsible AI practice and AI education are central to AI implementation: a rapid review for all medical imaging professionals in Europe," addresses the crucial intersection of artificial intelligence adoption, ethical considerations, and the necessity of education within European medical imaging. The authors conducted a rapid review of existing literature and resources to underscore that responsible AI practices and comprehensive AI education are fundamental for the successful and safe integration of AI technologies in radiology and radiography. By examining ethical frameworks, current educational initiatives, and the importance of collaboration among medical imaging professionals, the paper aims to guide Europe's healthcare sector towards a future where AI is implemented responsibly and effectively for the benefit of both practitioners and patients.

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Shoja, M. M., & al. (2023). The Emerging Role of Generative Artificial Intelligence in Medical Education, Research, and Practice

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Shoja, M. M., & al. (2023). The Emerging Role of Generative Artificial Intelligence in Medical Education, Research, and Practice

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Summary: This 2023 article by Shoja et al. explores the emerging capabilities and implications of generative artificial intelligence (GAI), particularly large language models like ChatGPT, within medical education, research, and clinical practice. The authors dissect the current trends, strengths, and limitations of using GAI in these areas, such as its potential in generating text and aiding research, alongside concerns about accuracy, plagiarism, and ethical considerations like authorship. Ultimately, the piece argues for the development of consensus-based guidelines to ensure the responsible and effective integration of GAI in medicine while acknowledging the need for human oversight.

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Sapci, A. H., & al. (2020). Artificial Intelligence Education and Tools for Medical and Health Informatics Students : Systematic Review

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Sapci, A. H., & al. (2020). Artificial Intelligence Education and Tools for Medical and Health Informatics Students : Systematic Review

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Summary: This source is a systematic review investigating the current landscape of artificial intelligence (AI) education and tools within medical and health informatics. The authors, A Hasan Sapci and H Aylin Sapci, aimed to evaluate existing AI training practices and the use of AI to enhance learning in these fields. Their analysis of published research highlights a growing recognition of the need to integrate AI training into the curricula for medical and health informatics students. Ultimately, the review concludes that standardised AI curricula and defined competencies are lacking, leading the authors to propose a framework for specialised AI training tailored to these disciplines.