Welcome to the METIS Toolkit
Welcome to the METIS Toolkit, an open-access learning resource designed to help students explore how emerging technologies are shaping the future of medical education and healthcare. The Toolkit brings together a diverse collection of lectures, presentation slides, practical workshop materials, and recommended further readings.
Covering topics such as Artificial Intelligence (AI), Virtual Reality (VR), simulation-based learning, digital health, ethics, and citizen science, the Toolkit connects technological innovation with the educational, clinical, and societal challenges of tomorrow.
Whether you are studying medicine, working in a health-related discipline, or simply curious about the role of technology in healthcare, the Toolkit offers a flexible way to discover new ideas, deepen your knowledge, and reflect on how these developments may influence your future studies and professional practice.
How to make the most of the Toolkit
You can explore the Toolkit at your own pace, depending on your interests, learning objectives, and available time.
- Start with a topic that interests you. Browse the thematic sections below and choose the lectures most relevant to your studies or questions.
- Watch the lecture recordings. Use the videos to gain an introduction to each topic, understand key concepts, and explore real-world examples.
- Review the presentation slides. Consult the slides alongside or after each recording to revisit important ideas and consolidate your learning.
- Go deeper with the recommended readings. Explore the additional articles, publications, and online resources to broaden your understanding and discover different perspectives.
- Experiment with the practical materials. Where available, use the workshop resources and tool demonstrations to explore how AI, VR, simulations, and other digital tools can be applied in medical education and healthcare.
You do not need to complete every resource in sequence. The Toolkit is intended to support both focused exploration of individual topics and a broader understanding of the relationship between technology, learning, and healthcare.
3 lectures
1. Artificial Intelligence and Digital Innovation in Healthcare
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Lecture 06
Introduction to Cybersecurity Trends in Healthcare
3 lectures
3. Ethics, Responsibility, Equity, and Trust in Digital Healthcare
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Lecture 04
Where Ethics Meets the Machine: Ethics of AI and Digitalisation in Healthcare
Lecture 08
The Patient in the Loop: Autonomy, Privacy and Responsibility in Digital Healthcare
Lecture 11
Who Benefits? Justice, Discrimination, and Better Health Outcomes in Digital Healthcare
6 lectures
2. Emerging Technologies in Medical Education
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Lecture 02
Emerging Technologies in Health Professionals’ Education
Lecture 05
Virtual Dissections in Human Anatomy Teachings
Lecture 07
Gamification Approach in Human Anatomy Teachings
Lecture 10
Medical Education in the Age of ICT and AI: Rethinking Learning and Teaching Beyond Directed Instruction
Lecture 13
Peer-to-peer Tutoring Activities in Human Anatomy with Virtual Tools and Flipped Classroom Approaches
3 lectures
4. Patients, Citizens, and Participatory Approaches to Healthcare
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Lecture 09
Mobilising Citizen Science in Support of Health Research and Health Services Provision
Lecture 14
The “Personalization from Below” Paradigm: Citizen Science-Based Data Altruism Practices to Rethink Health Services and Personalization
Lecture 15
Medical citizen science: From data awareness to advocacy
Hands-on workshops
Hands-on workshop 2
Citizen Science-Based Data Altruism Practices to Rethink Health Services and Personalization
Hands-on workshop 3
Intelligent Assistant for Student Inquiry Support (IASIS)
Hands-on workshop 4
A principle-based approach to the ethical evaluation of digital and AI-based healthcare applications
Hands-on workshop 5
Virtual dissection tool for anatomy learning
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Lecture 01 · Extra reading
AI and Diagnostic Assistance
Suggested readings and additional resources for this lecture.
Readings
Singh, S. P., Singh, P., Diwakar, M., & Ravi, V. (2025). Data-driven diagnostics and disease prediction with AI optimization.
Collecchia, G., & De Gobbi, R. (2023). AI in clinical practice: A guide to artificial intelligence and digital medicine.
Hirosawa, T. (2025). Artificial intelligence in medical diagnostics.
Toma, M. (2025). AI-assisted medical diagnostics: A clinical guide to next-generation diagnostics.
Farrokhi, M., Taheri, F., Adibnia, E., Ranjbar, Z., & Hashemi, E. (2024). The AI diagnostician: Improving medical diagnosis with artificial intelligence.
Other resources
Robust AI tools predict future cancer.
Video resource on AI and medical diagnosis.
Video resource on AI and medical diagnosis.
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Lecture 02 · Extra reading
Emerging Technologies in Health Professionals’ Education
Suggested scholarly papers and additional resources for this lecture.
Koivisto, J-M.*, Kämäräinen, S.*, Mattsson., K., Jumisko-Pyykkö, S., Ikonen., R. & Haavisto, E. 2025. Exploring Nursing Students’ Experiences of Empathy and User Experiences in an Immersive Virtual Reality Simulation Game: Cross-sectional Sectional Study.
Koivisto, J-M., Buure, T., Rosqvist, K., Engblom, J. & Haavisto, E. 2023. The effectiveness of simulation game on nursing students’ surgical nursing knowledge — a quasiexperimental study.
Mattsson, K., Haavisto, E. & Koivisto, J-M. 2025. Embracing opportunities and addressing barriers in learning empathy through VR simulation games - a qualitative descriptive study of nursing students’ experiences.
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Lecture 03 · Extra reading
A Stroll Along AI
Suggested readings and additional resources for this lecture.
Readings
Turing, A. M. (1950). Computing machinery and intelligence.
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks.
Karras, T., Laine, S., & Aila, T. (2019). A style-based generator architecture for generative adversarial networks.
Other resources
Artificial intelligence.
About DeepMind.
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Lecture 04 · Extra reading
Where Ethics Meets the Machine
Suggested readings, PDFs and additional resources for this lecture.
Beauchamp, T. L. & Childress, J. F. (1979). Principles of biomedical ethics.
Cobler-Lichter, M., Delamater, J. M., Teixeira, F. J. P., Reyes, A. M., Arderi, T. R., Manolowitz, B., McKeown, J. A., Koru-Sengul, T., Jagid, J., Graciolli Cordeiro, J., Massed, N., Kottapally, M., Merenda, A., O'Phelan, K., Namias, N. & Alkhachroum, A. (2025). Machine learning models to predict withdrawal of life-sustaining therapy in patients with severe traumatic brain injury.
European Commission. (2019). Ethics Guidelines for Trustworthy AI.
Gander, J. C., Basu, M., McPherson, L., Garber, M. D., Pastan, S. O., Manatunga, A., Jacob Arriola, K. & Patzer, R. E. (2018). iChoose Kidney for treatment options: Updated models for shared decision aid.
Obermeyer, Z., Powers, B., Vogeli, C. & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations.
Pruinelli, L., Balakrishnan, K., Ma, S., Li, Z., Wall, A., Lai, J. C., Schold, J. D., Pruett, T. & Simon, G. (2025). Transforming liver transplant allocation with artificial intelligence and machine learning: a systematic review.
Thorsen-Meyer, H.-C., Nielsen, A. B., Nielsen, A. P., Kaas-Hansen, B. S., Toft, P., Schierbeck, J., Strøm, T., Chmura, P. J., Heimann, M., Dybdahl, L., Spangsege, L., Hulsen, P., Belling, K., Brunak, S. & Perner, A. (2020). Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit: a retrospective study of high-frequency data in electronic patient records.
Topo, E. J. (2019). Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again.
United Nations Educational, Scientific and Cultural Organisation (UNESCO). (2022). Recommendation on the Ethics of Artificial Intelligence.
World Health Organization (WHO). (2021). Ethics and Governance of Artificial Intelligence for Health: WHO Guidance.
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Lecture 05 · Extra reading
Virtual Dissections in Human Anatomy Teachings
Suggested readings and additional resources for this lecture.
Al-Rubaie, A. (2025). From cadavers to codes: The evolution of anatomy education through digital technologies.
Castellano, M. S., Contreras-McKay, I., Neyem, A., Farfán, E., Inzunza, O., Ottone, N. E., del Sol, M., Alario-Hoyos, C., Alvarado, M. S., & Tubbs, R. S. (2024). Empowering human anatomy education through gamification and artificial intelligence: An innovative approach to knowledge appropriation.
Cugelman, B. (2013). Gamification: What it is and why it matters to digital health behavior change developers.
Gentry, S. V., Gauthier, A., L’Estrade Ehrstrom, B., Wortley, D., Lilienthal, A., Tudor Car, L., Dauwels-Okutsu, S., Nikolaou, C. K., Zary, N., Campbell, J., & Car, J. (2019). Serious gaming and gamification education in health professions: Systematic review.
Pawar, V. V., & Pawar, D. (2024). Gamification in medical education: A new approach.
Xu, M., Luo, Y., Zhang, Y., Xia, R., Qian, H., & Zou, X. (2023). Game-based learning in medical education.
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Lecture 06 · Extra reading
Introduction to Cybersecurity Trends in Healthcare
Suggested reading and additional resources for this lecture.
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Lecture 07 · Extra reading
Gamification Approach in Human Anatomy Teachings
Suggested readings and additional resources for this lecture.
Al-Rubaie, A. (2025). From cadavers to codes: The evolution of anatomy education through digital technologies.
Castellano, M. S., Contreras-McKay, I., Neyem, A., Farfán, E., Inzunza, O., Ottone, N. E., del Sol, M., Alario-Hoyos, C., Alvarado, M. S., & Tubbs, R. S. (2024). Empowering human anatomy education through gamification and artificial intelligence: An innovative approach to knowledge appropriation.
Cugelman, B. (2013). Gamification: What it is and why it matters to digital health behavior change developers.
Gentry, S. V., Gauthier, A., L’Estrade Ehrstrom, B., Wortley, D., Lilienthal, A., Tudor Car, L., Dauwels-Okutsu, S., Nikolaou, C. K., Zary, N., Campbell, J., & Car, J. (2019). Serious gaming and gamification education in health professions: Systematic review.
Pawar, V. V., & Pawar, D. (2024). Gamification in medical education: A new approach.
Xu, M., Luo, Y., Zhang, Y., Xia, R., Qian, H., & Zou, X. (2023). Game-based learning in medical education.
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Lecture 08 · Extra reading
The Patient in the Loop
Suggested readings and additional resources for this lecture.
Readings
Bower, J. L., & Christensen, C. M. (1995). Disruptive technologies: Catching the wave.
Gong, C., & Ribiere, V. (2021). Developing a unified definition of digital transformation.
McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., Back, T., Chesus, M., Corrado, G. S., Darzi, A., Etemadi, M., Garcia-Vicente, F., Gilbert, F. J., Halling-Brown, M., Hassabis, D., Jansen, S., Karthikesalingam, A., Kelly, C. J., King, D., & Shetty, S. (2020). International evaluation of an AI system for breast cancer screening.
Prainsack, B., El-Sayed, S., Forgó, N., Szoszkiewicz, Ł., & Baumer, P. (2022). Data solidarity: A blueprint for governing health futures.
Rocher, L., Hendrickx, J. M., & de Montjoye, Y.-A. (2019). Estimating the success of re-identifications in incomplete datasets using generative models.
Other resources
The outdated machine hampering the fight against Covid-19.
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Lecture 09 · Extra reading
Mobilising Citizen Science in Support of Health Research and Health Services Provision
Suggested readings and additional resources for this lecture.
Readings
Berti Suman, A., Heyen, N. B., & Micheli, M. (2023). Reimagining health services provision for neglected groups: The “personalization from below” phenomenon.
Berti Suman, A., & Pierce, R. (2018). Challenges for citizen science and the EU open science agenda under the GDPR.
Other resources
ECSA Working Group: Citizen Science for Health
Data altruism organisations
Citizen Science Toolkit
Isala
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Lecture 10 · Extra reading
Medical Education in the Age of ICT and AI
Suggested readings and additional resources for this lecture.
Readings
Serres, M. (2019). C’était mieux avant !
Densen, P. (2011). Challenges and opportunities facing medical education.
Bruner, J. S. (1960). The process of education.
Bjork, R. A., & Bjork, E. L. (2011). Making things hard on yourself, but in a good way: Creating desirable difficulties to enhance learning.
Croskerry, P. (2003). The importance of cognitive errors in diagnosis and strategies to minimize them.
Clark, A., & Chalmers, D. (1998). The extended mind.
Other resources
Puentedura, R. R. (n.d.). SAMR model: A contextualized introduction.
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Lecture 11 · Extra reading
Who Benefits? Justice, Discrimination, and Better Health Outcomes in Digital Healthcare
Suggested readings and additional resources for this lecture.
Ayadi, H., Bour, C., Fischer, A., Ghoniem, M. & Fagherazzi, G. (2023). The Long COVID experience from a patient's perspective: A clustering analysis of 27,216 Reddit posts.
Criado Perez, C. (2019). Invisible Women: Exposing Data Bias in a World Designed for Men.
Daneshjou, R., Vodrahalli, K., Novoa, R. A., Jenkins, M., Liang, W., Rotemberg, V., Ko, J., Swetter, S. M., Bailey, E. E., Gevaert, O., Carlini, A., Frias, A. & Zou, J. (2022). Disparities in dermatology AI performance on a diverse, curated clinical image set.
Fricker, M. (2007). Epistemic Injustice: Power and the Ethics of Knowing.
Hsu, J. (2019). Medical advice from a bot: The unproven promise of Babylon Health.
Mukwende, M., Tamony, P. & Turner, M. (2020). Mind the Gap: A Handbook of Clinical Signs in Black and Brown Skin.
Obermeyer, Z., Powers, B., Vogeli, C. & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations.
Sieck, C. J., Sheon, A., Ancker, J. S., Castek, J., Callahan, B. & Siefer, A. (2021). Digital inclusion as a social determinant of health.
Zawati, M. H. & Lang, M. (2024). Does an app a day keep the doctor away? AI symptom checker applications, entrenched bias, and professional responsibility.
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Lecture 12 · Extra reading
Immersive Surgical Education
Suggested reading and additional resource for this lecture.
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Lecture 13 · Extra reading
Peer-to-peer Tutoring Activities in Human Anatomy with Virtual Tools and Flipped Classroom Approaches
Suggested readings and additional resources for this lecture.
Aquino, M., Santamaria, J., Quadri, E., Riegsecker, B., Li, J. J., Kim, J. J., Nausheen, F., & Han, V. (2024). Comparing peer-taught student tutors to faculty-taught student tutors in educating medical students on musculoskeletal ultrasound.
Green, R. A., Cates, T., White, L., & Farchione, D. (2016). Do collaborative practical tests encourage student-centered active learning of gross anatomy?
Jha, S., Sethi, R., Kumar, M., & Khorwal, G. (2024). Comparative study of the flipped classroom and traditional lecture methods in anatomy teaching.
Joseph, M. A., Roach, E. J., Natarajan, J., Karkada, S., & Cayaban, A. R. R. (2021). Flipped classroom improves Omani nursing students’ performance and satisfaction in anatomy and physiology.
Kazeminia, M., Salehi, L., Khosravipour, M., & Rajati, F. (2022). Investigation flipped classroom effectiveness in teaching anatomy: A systematic review.
Morris, T. J., Ruvina, M., Cooper, C. E. A., Fukuda, N., Berger, H., Wagner, D. F., Allison, S., & Woodcock, J. (2025). Nerves of steel: Bolstering student confidence in gross anatomy through a peer-to-peer intervention.
Wang, L., Du, B., Fang, D., Gao, Y., & Liu, L. (2024). Flipped classroom assisted by Rain Classroom for anatomy practical classes: Challenges and opportunities of anatomy education.
Xiao, J., & Adnan, S. (2022). Flipped anatomy classroom integrating multimodal digital resources shows positive influence upon students’ experience and learning performance.
Yang, C., Yang, X., Yang, H., & Fan, Y. (2020). Flipped classroom combined with human anatomy web-based learning system shows promising effects in anatomy education.
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Lecture 14 · Extra reading
The “Personalization from Below” Paradigm
Suggested readings and additional resources for this lecture.
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Lecture 15 · Extra reading
Medical citizen science: From data awareness to advocacy
Suggested readings and additional resources for this lecture.
Sarrazin-Gendron, R., Ghasemloo Gheidari, P., Butyaev, A., et al. (2024). Improving microbial phylogeny with citizen science within a mass-market video game.
Waldispühl, J., Szantner, A., Knight, R., et al. (2020). Leveling up citizen science.
Colomer-Lahiguera, S., et al. (2023). Patient and public involvement in cancer research: A scoping review.
Fiske, A., Prainsack, B., & Buyx, A. (2019). Meeting the needs of underserved populations: Setting the agenda for more inclusive citizen science of medicine.
Wright, M. T., Springett, J., & Kongats, K. (2018). What is participatory health research?
Marlett, N., Shklarov, S., Marshall, D., Santana, M. J., & Wasylak, T. (2015). Building new roles and relationships in research: A model of patient engagement research.
Smith, E., Bélisle-Pipon, J.-C., & Resnik, D. (2019). Patients as research partners: How to value their perceptions, contribution and labor?
Rouleau, G., Bélisle-Pipon, J.-C., Birko, S., Karazivan, P., Fernandez, N., Bilodeau, K., Chao, Y.-S., de Pokomandy, A., Foley, V., Gagnon, B., Gontijo Guerra, S., Khanji, C., Lamoureux-Lamarche, C., Lebouché, B., Lunghi, C., Menear, M., Riverin, B. D., & Rodrigue, C. (2018). Early career researchers’ perspectives and roles in patient-oriented research.
Heyen, N. B., Gardecki, J., Eidt-Koch, D., Schlangen, M., Pauly, S., Eickmeier, O., Wagner, T., & Bratan, T. (2022). Patient science: Citizen science involving chronically ill people as co-researchers.
Wiggins, A., & Wilbanks, J. (2019). The rise of citizen science in health and biomedical research.
Hall, L. K., Kunz, B. F., Davis, E. V., Dawson, R. I., & Powers, R. S. (2015). The Cancer Experience Map: An approach to including the patient voice in supportive care solutions.
Evans, B. J. (2017). Power to the people: Data citizens in the age of precision medicine.
Cheung, A. (2018). Moving beyond consent for citizen science in big data health and medical research.
Shahid, A., Lalani, I. N., Rosgen, B. K., Sept, B. G., Longmore, S., Parsons Leigh, J., Stelfox, H. T., & Fiest, K. M. (2022). A scoping review of methods to measure and evaluate citizen engagement in health research.
Spears, P. A. (2021). Patient engagement in cancer research from the patient’s perspective.
Gagliardi, A. R., Lemieux-Charles, L., Brown, A. D., Sullivan, T., & Goel, V. (2008). Barriers to patient involvement in health service planning and evaluation: An exploratory study.
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Workshop · Extra reading
Intelligent Assistant for Student Inquiry Support (IASIS)
Suggested readings and additional resources for this workshop.
Batista, J., Mesquita, A., & Carnaz, G. (2024). Generative AI and higher education: Trends, challenges, and future directions from a systematic literature review.
Yusuf, A., Pervin, N., & Román-González, M. (2024). Generative AI and the future of higher education: A threat to academic integrity or reformation? Evidence from multicultural perspectives.
Nguyen, K. V. (2025). The use of generative AI tools in higher education: Ethical and pedagogical principles.
Boscardin, C. K., Gin, B., Golde, P. B., & Hauer, K. E. (2024). ChatGPT and generative artificial intelligence for medical education: Potential impact and opportunity.
Dave, M., & Patel, N. (2023). Artificial intelligence in healthcare and education.
Masters, K. (2019). Artificial intelligence in medical education.
Capozucca, A., Yampolskyi, D., Goldberg, A., & Cristiá, M. (2025). Do AI assistants help students write formal specifications? A study with ChatGPT and the B-Method.
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Workshop · Extra reading
Virtual Dissection Tool for Anatomy Learning
Suggested readings and additional resources for this workshop.
Ackerman, M. J. (1998). The Visible Human Project.
Ackerman, M. J. (2016). The Visible Human Project®: From body to bits.
Ackerman, M. J. (2017). The Visible Human Project: From body to bits.
Chung, B. S., Chung, M. S., & Park, J. S. (2020). Portable document format file containing the schematics and operable surface models of the head structures.
Chung, B. S., Park, H. S., Park, J. S., Hwang, S. B., & Chung, M. S. (2021). Sectioned and segmented images of the male whole body, female whole body, male head, and female pelvis from the Visible Korean.
Kim, J. Y., Chung, M. S., Hwang, W. S., Park, J. S., & Park, H. S. (2002). Visible Korean Human: Another trial for making serially-sectioned images.
Kwon, K., Shin, D. S., Shin, B.-S., Park, H. S., Lee, S., Jang, H. G., Park, J. S., & Chung, M. S. (2015). Virtual endoscopic and laparoscopic exploration of stomach wall based on a cadaver’s sectioned images.
Noetscher, G. M., Htet, A. T., Maino, N. D., & Lacroix, P. A. (2017). The Visible Human Project male CAD based computational phantom and its use in bioelectromagnetic simulations.
Park, H. S., Choi, D. H., & Park, J. S. (2015). Improved sectioned images and surface models of the whole female body.
Park, J. S., Chung, M. S., Hwang, S. B., Lee, Y. S., Har, D.-H., & Park, H. S. (2005). Visible Korean human: Improved serially sectioned images of the entire body.
Park, J. S., Chung, M. S., Hwang, S. B., Shin, B.-S., & Park, H. S. (2006). Visible Korean Human: Its techniques and applications.
Spitzer, V., Ackerman, M. J., Scherzinger, A. L., & Whitlock, D. G. (1996). The Visible Human Male: A technical report.
Spitzer, V. M., & Whitlock, D. G. (1998). The Visible Human Dataset: The anatomical platform for human simulation.
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Workshop · Extra reading
Citizen Science-Based Data Altruism Practices to Rethink Health Services and Personalization
Suggested readings and additional resources for this workshop.
Readings
Slade, M., Todowede, O., Boyd, D., Ewart, C., Hara, A., Higton, F., Moran, S., Repper, J., Robotham, D., Slade, E., Sweeney, A., & Rennick-Egglestone, S. (2025). Living with mental health issues: Citizen science project on self-management strategies.
Schachtner, C., & Baumann, N. (2026). Data cooperatives and their impact on GovTech as a regional development approach for digital transformation at a local level.
Lebeer, S., Ahannach, S., Gehrmann, T., Wittouck, S., Eilers, T., Oerlemans, E. F. M., Condori, S., Dillen, J., Spacova, I., Vander Donck, L., Donders, G. G. G., Couck, L., Goovaerts, E., Nivelle, O., Van den Broeck, I., Morre, S. A., Hervé, M. V., De Baetselier, I., Molenberghs, G., & Reid, G. (2023). A citizen-science-enabled catalogue of the vaginal microbiome and associated factors.
Berti Suman, A., Heyen, N. B., & Micheli, M. (2023). Reimagining health services provision for neglected groups: The “personalization from below” phenomenon.
Other resources
MIDATA Cooperative
ALMA. (n.d.). Smart underwear.
The Isala Sisterhood. (2024). The Isala Sisterhood.