KCU Libraries: Introduction to AI: AI in Medical Education

AI in MedED Videos

AI Library Resources

KCU Libraries are always investigating and evaluating options to improve resources for the KCU community. We currently have the following AI tools available through our databases:

Scopus AI: Scopus AI is a search tool powered by genAI that provides summaries of articles or sets of articles, produces concept maps, and allows you to sort search results by various categories, such as most influential.
- Link to more information from Scopus on the assistant
Web of Science Research Assistant: A generative Al-powered tool that provides summaries of articles or sets of articles, produces concept maps and other graphical depictions of search results, and provides prompts to guides your next steps in the search process.
- Link to more information from Clarivate on the assistant

Books

Deep Medicine by Eric Topol
ISBN: 9781541644632
Publication Date: 2019
AI has the potential to transform everything doctors do, from notetaking and medical scans to diagnosis and treatment, greatly cutting down the cost of medicine and reducing human mortality.
Algorithms of Oppression by Safiya Umoja Noble
ISBN: 9781479849949
Publication Date: 2018
Data discrimination is a real social problem. This book is a revealing look at how negative biases are embedded in search engine results and algorithms
Artificial Intelligence in Surgery: Understanding the Role of AI in Surgical Practice by Daniel A. Hashimoto; Guy Rosman; Ozanan R. Meireles
ISBN: 9781260452730
Publication Date: 2021
Build a solid foundation in surgical AI with this engaging, comprehensive guide for AI novices
Artificial Intelligence: Ethical, Social, and Security Impacts for the Present and the Future by IT Governance Publishing (Editor)
ISBN: 9781787783706
Publication Date: 2022
This book provides a global perspective on AI, focusing on the digital ethics surrounding AI technology.
Artificial Intelligence : Reflections in Philosophy, Theology, and the Social Sciences by Benedikt Paul Goecke & Astrid Marieke Rosenthal-von der Pütten
ISBN: 9783957437488
Publication Date: 2020
This book discusses major issues of the current AI debate from the perspectives of philosophy, theology, and the social sciences: Can AI have a consciousness? Is superintelligence possible and probable? How does AI change individual and social life? Can there be artificial persons? What influence does AI have on religious worldviews?
Brave New Words by Salman Khan
ISBN: 9780593656952
Publication Date: 2024
From the founder of Khan Academy, this book looks at the AI revolution in education and offers a road map for teachers, parents, and students to navigate this challenging new world.

e-Journals

Examples of AI in Medical Education

Clinical Decision Support Systems (CDSS) Training

Medical schools are introducing AI-based Clinical Decision Support Systems (CDSS) to help students learn how to diagnose and treat patients more effectively. AI models are used to analyze patient data, suggest diagnoses, and recommend treatment plans.

Radiology and Imaging Analysis

AI tools for medical imaging, such as deep learning algorithms that assist in detecting abnormalities in X-rays, MRIs, and CT scans, are now part of radiology training.

Virtual Patient Simulations

AI-driven virtual patient simulations allow medical students to interact with AI-powered avatars representing patients with different conditions, allowing for realistic diagnostic practice.

Predictive Analytics and Population Health

AI is being used to teach students how to analyze large datasets to predict disease outbreaks, patient outcomes, and hospital resource needs, giving students exposure to population health management.

Personalized Medicine and Genomics

AI's role in personalized medicine is integrated into medical genomics curricula, where students learn how machine learning algorithms analyze genetic data to predict disease risk or recommend tailored treatments.

Natural Language Processing (NLP) for Medical Documentation

Medical schools are integrating AI-driven Natural Language Processing (NLP) tools into their teaching to help students learn how to efficiently document patient interactions and extract meaningful insights from unstructured data like electronic health records (EHRs).

Ethics and AI in Medicine

Medical ethics courses are evolving to address the implications of AI in healthcare, teaching students about the ethical challenges of AI-based decision-making, data privacy, and the impact of AI on the patient-provider relationship.

Surgical Training and Robotic Assistance

AI and robotic systems are being used to help students in surgical training, providing them with real-time feedback and guidance during simulated procedures

Articles on AI in Medical Education

Teaching AI Ethics in Medical Education: A Scoping Review of Current Literature and Practices Abstract Introduction: The increasing use of Artificial Intelligence (AI) in medicine has raised ethical concerns, such as patient autonomy, bias, and transparency. Recent studies suggest a need for teaching AI ethics as part of medical curricula. This scoping review aimed to represent and synthesize the literature on teaching AI ethics as part of medical education. Methods: The PRISMA-SCR guidelines and JBI methodology guided a literature search in four databases (PubMed, Embase, Scopus, and Web of Science) for the past 22 years (2000-2022). To account for the release of AI-based chat applications, such as ChatGPT, the literature search was updated to include publications until the end of June 2023. Results: 1384 publications were originally identified and, after screening titles and abstracts, the full text of 87 publications was assessed. Following the assessment of the full text, 10 publications were included for further analysis. The updated literature search identified two additional relevant publications from 2023 were identified and included in the analysis. All 12 publications recommended teaching AI ethics in medical curricula due to the potential implications of AI in medicine. Anticipated ethical challenges such as bias were identified as the recommended basis for teaching content in addition to basic principles of medical ethics. Case-based teaching using real-world examples in interactive seminars and small groups was recommended as a teaching modality. Conclusion: This scoping review reveals a scarcity of literature on teaching AI ethics in medical education, with most of the available literature being recent and theoretical. These findings emphasize the importance of more empirical studies and foundational definitions of AI ethics to guide the development of teaching content and modalities. Recognizing AI's significant impact of AI on medicine, additional research on the teaching of AI ethics in medical education is needed to best prepare medical students for future ethical challenges.
Generative AI (gAI) in medical education: Chat-GPT and co “The use of chatbots in medical education is an emerging trend that is welcomed by many educators and medical professionals. In particular, the use of ChatGPT, a large language model of OpenAI, offers a variety of benefits for students and educators alike […]” [1]. So far so amazing, the passage already points to the whole dilemma: will teaching at universities ever be the same after ChatGPT as it never was anyways?
Applications of Artificial Intelligence (AI) in Medical Education: A Scoping Review Abstract Artificial Intelligence (AI) is increasingly used to support medical students' learning journeys, providing personalized experiences and improved outcomes. We conducted a scoping review to explore the current application and classifications of AI in medical education. Following the PRISMA-P guidelines, we searched four databases, ultimately including 22 studies. Our analysis identified four AI methods used in various medical education domains, with the majority of applications found in training labs. The use of AI in medical education has the potential to improve patient outcomes by equipping healthcare professionals with better skills and knowledge. Post-implementation refers to the outcomes of AI-based training, which showed improved practical skills among medical students. This scoping review highlights the need for further research to explore the effectiveness of AI applications in different aspects of medical education.
Global cross-sectional student survey on AI in medical, dental, and veterinary education and practice at 192 faculties Abstract Background: The successful integration of artificial intelligence (AI) in healthcare depends on the global perspectives of all stakeholders. This study aims to answer the research question: What are the attitudes of medical, dental, and veterinary students towards AI in education and practice, and what are the regional differences in these perceptions? Methods: An anonymous online survey was developed based on a literature review and expert panel discussions. The survey assessed students' AI knowledge, attitudes towards AI in healthcare, current state of AI education, and preferences for AI teaching. It consisted of 16 multiple-choice items, eight demographic queries, and one free-field comment section. Medical, dental, and veterinary students from various countries were invited to participate via faculty newsletters and courses. The survey measured technological literacy, AI knowledge, current state of AI education, preferences for AI teaching, and attitudes towards AI in healthcare using Likert scales. Data were analyzed using descriptive statistics, Mann-Whitney U-test, Kruskal-Wallis test, and Dunn-Bonferroni post hoc test. Results: The survey included 4313 medical, 205 dentistry, and 78 veterinary students from 192 faculties and 48 countries. Most participants were from Europe (51.1%), followed by North/South America (23.3%) and Asia (21.3%). Students reported positive attitudes towards AI in healthcare (median: 4, IQR: 3-4) and a desire for more AI teaching (median: 4, IQR: 4-5). However, they had limited AI knowledge (median: 2, IQR: 2-2), lack of AI courses (76.3%), and felt unprepared to use AI in their careers (median: 2, IQR: 1-3). Subgroup analyses revealed significant differences between the Global North and South (r = 0.025 to 0.185, all P < .001) and across continents (r = 0.301 to 0.531, all P < .001), with generally small effect sizes. Conclusions: This large-scale international survey highlights medical, dental, and veterinary students' positive perceptions of AI in healthcare, their strong desire for AI education, and the current lack of AI teaching in medical curricula worldwide. The study identifies a need for integrating AI education into medical curricula, considering regional differences in perceptions and educational needs. Trial registration: Not applicable (no clinical trial). Keywords: Artificial intelligence; Cross-sectional studies; Curriculum; Education; Medical; Students; Surveys and questionnaires.
Artificial intelligence in medical education Abstract Artificial intelligence (AI) is a growing phenomenon, and will soon facilitate wide-scale changes in many professions, including medical education. In order for medical educators to be properly prepared for AI, they will need to have at least a fundamental knowledge of AI in relation to learning and teaching, and the extent to which it will impact on medical education. This Guide begins by introducing the broad concepts of AI by using fairly well-known examples to illustrate AI's implications within the context of education. It then considers the impact of AI on medicine and the implications of this impact for educators trying to educate future doctors. Drawing on these strands, it then identifies AI's direct impact on the methodology and content of medical education, in an attempt to prepare medical educators for the changing demands and opportunities that are about to face them because of AI.
Artificial Intelligence in Undergraduate Medical Education: A Scoping Review Abstract Purpose: Artificial intelligence (AI) is a rapidly growing phenomenon poised to instigate large-scale changes in medicine. However, medical education has not kept pace with the rapid advancements of AI. Despite several calls to action, the adoption of teaching on AI in undergraduate medical education (UME) has been limited. This scoping review aims to identify gaps and key themes in the peer-reviewed literature on AI training in UME. Method: The scoping review was informed by Arksey and O'Malley's methodology. Seven electronic databases including MEDLINE and EMBASE were searched for articles discussing the inclusion of AI in UME between January 2000 and July 2020. A total of 4,299 articles were independently screened by 3 co-investigators and 22 full-text articles were included. Data were extracted using a standardized checklist. Themes were identified using iterative thematic analysis. Results: The literature addressed: (1) a need for an AI curriculum in UME, (2) recommendations for AI curricular content including machine learning literacy and AI ethics, (3) suggestions for curriculum delivery, (4) an emphasis on cultivating "uniquely human skills" such as empathy in response to AI-driven changes, and (5) challenges with introducing an AI curriculum in UME. However, there was considerable heterogeneity and poor consensus across studies regarding AI curricular content and delivery. Conclusions: Despite the large volume of literature, there is little consensus on what and how to teach AI in UME. Further research is needed to address these discrepancies and create a standardized framework of competencies that can facilitate greater adoption and implementation of a standardized AI curriculum in UME.
AI-Enabled Medical Education: Threads of Change, Promising Futures, and Risky Realities Across Four Potential Future Worlds Abstract Background: The rapid trajectory of artificial intelligence (AI) development and advancement is quickly outpacing society's ability to determine its future role. As AI continues to transform various aspects of our lives, one critical question arises for medical education: what will be the nature of education, teaching, and learning in a future world where the acquisition, retention, and application of knowledge in the traditional sense are fundamentally altered by AI? Objective: The purpose of this perspective is to plan for the intersection of health care and medical education in the future. Methods: We used GPT-4 and scenario-based strategic planning techniques to craft 4 hypothetical future worlds influenced by AI's integration into health care and medical education. This method, used by organizations such as Shell and the Accreditation Council for Graduate Medical Education, assesses readiness for alternative futures and effectively manages uncertainty, risk, and opportunity. The detailed scenarios provide insights into potential environments the medical profession may face and lay the foundation for hypothesis generation and idea-building regarding responsible AI implementation. Results: The following 4 worlds were created using OpenAI's GPT model: AI Harmony, AI conflict, The world of Ecological Balance, and Existential Risk. Risks include disinformation and misinformation, loss of privacy, widening inequity, erosion of human autonomy, and ethical dilemmas. Benefits involve improved efficiency, personalized interventions, enhanced collaboration, early detection, and accelerated research. Conclusions: To ensure responsible AI use, the authors suggest focusing on 3 key areas: developing a robust ethical framework, fostering interdisciplinary collaboration, and investing in education and training. A strong ethical framework emphasizes patient safety, privacy, and autonomy while promoting equity and inclusivity. Interdisciplinary collaboration encourages cooperation among various experts in developing and implementing AI technologies, ensuring that they address the complex needs and challenges in health care and medical education. Investing in education and training prepares professionals and trainees with necessary skills and knowledge to effectively use and critically evaluate AI technologies. The integration of AI in health care and medical education presents a critical juncture between transformative advancements and significant risks. By working together to address both immediate and long-term risks and consequences, we can ensure that AI integration leads to a more equitable, sustainable, and prosperous future for both health care and medical education. As we engage with AI technologies, our collective actions will ultimately determine the state of the future of health care and medical education to harness AI's power while ensuring the safety and well-being of humanity. Keywords: ChatGPT; GPT-4; Open-AI; OpenAI; artificial intelligence; autonomous; autonomy; ethic; ethical; ethics; ethics and AI; future; future of healthcare; generative; medical education; privacy; scenario; scenario planning; strategic planning.
The rise of ChatGPT: Exploring its potential in medical education Abstract The integration of artificial intelligence (AI) into medical education has the potential to revolutionize the way students learn about biomedical sciences. Large language models, such as ChatGPT, can serve as virtual teaching assistants, providing students with detailed and relevant information and perhaps eventually interactive simulations. ChatGPT has the potential to increase student engagement and enhance student learning, though research is needed to confirm this. The challenges and limitations of ChatGPT must also be considered, including ethical issues and potentially harmful effects. It is crucial for medical educators to keep pace with technology's rapidly changing landscape and consider the implications for curriculum design, assessment strategies, and teaching methods. Continued research and evaluation are necessary to ensure the optimal integration of AI-based learning tools into medical education.
ChatGPT and Generative Artificial Intelligence for Medical Education: Potential Impact and Opportunity Abstract ChatGPT has ushered in a new era of artificial intelligence (AI) that already has significant consequences for many industries, including health care and education. Generative AI tools, such as ChatGPT, refer to AI that is designed to create or generate new content, such as text, images, or music, from their trained parameters. With free access online and an easy-to-use conversational interface, ChatGPT quickly accumulated more than 100 million users within the first few months of its launch. Recent headlines in the popular press have ignited concerns relevant to medical education over the possible implications of cheating and plagiarism in assessments as well as excitement over new opportunities for learning, assessment, and research. In this Scholarly Perspective, the authors offer insights and recommendations about generative AI for medical educators based on literature review, including the AI literacy framework. The authors provide a definition of generative AI, introduce an AI literacy framework and competencies, and offer considerations for potential impacts and opportunities to optimize integration of generative AI for admissions, learning, assessment, and medical education research to help medical educators navigate and start planning for this new environment. As generative AI tools continue to expand, educators need to increase their AI literacy through education and vigilance around new advances in the technology and serve as stewards of AI literacy to foster social responsibility and ethical awareness around the use of AI.
The fundamentals of Artificial Intelligence in medical education research: AMEE Guide No. 156 Abstract The use of Artificial Intelligence (AI) in medical education has the potential to facilitate complicated tasks and improve efficiency. For example, AI could help automate assessment of written responses, or provide feedback on medical image interpretations with excellent reliability. While applications of AI in learning, instruction, and assessment are growing, further exploration is still required. There exist few conceptual or methodological guides for medical educators wishing to evaluate or engage in AI research. In this guide, we aim to: 1) describe practical considerations involved in reading and conducting studies in medical education using AI, 2) define basic terminology and 3) identify which medical education problems and data are ideally-suited for using AI. Keywords: Artificial Intelligence; assessment; big data; machine learning; medical education.

Additional Resources

AAMC Resources

The landscape of artificial intelligence (AI) is quickly evolving. Advances in this technology have led to new opportunities as well as questions and challenges around best practices and ethical use.

Medical schools and academic health systems across the country are making concerted efforts to integrate AI into education, clinical delivery and practice, and biomedical research. The AAMC is supporting its member institutions as they determine their individual approaches to this new technology, in navigating the initiation, planning, and implementation of any AI tools, and in monitoring the policy and regulatory environment.

Below is an overview of the AAMC’s work as well as opportunities for you to connect with your peers, share your experiences, and learn from experts.

Harvard Medical School Trends in Medicine: 5 Ways Medical Educators Can Use AI and Other Technologies

NYU Langone Health NewsHub: Artificial Intelligence Supercharges Learning for Students at NYU Grossman School of Medicine

A.T. Still University ATSU News: The AI generation: ATSU integrates the latest technologies to educate healthcare professionals of the future

The DO: The future of AI in medicine is osteopathic