AI in Healthcare. Hype or Help?
Discover how Artificial Intelligence (AI) is revolutionizing healthcare in this exciting course. The added value of AI for healthcare is explained from the healthcare professional's perspective, while basic AI principles are clarified from the AI developer's point of view.
Practical information:
Want to register?
- Prerequisites: Basic High-school mathematics and interest for Healthcare
- Price: Free
The course is free of charge, but the certificate costs €74.
Georganiseerd door:
Leertraject
About this course
Work and research in healthcare are evolving rapidly, partly because of innovative technology that is introduced due to a far-reaching digitization of the health sector. AI is one of these technologies that already influences healthcare today for the benefit of patients, professionals, and scholars, and it will continue to do so in the future. Therefore, current and future professionals and scholars, like yourself, will be challenged with the correct adoption of AI in healthcare.
This course is intended to empower you as a healthcare professional or scholar with insights and critical knowledge on the AI era, general principles and concepts of AI, the added value of AI in healthcare and associated data, ethics and regulatory boundaries. The course considers AI in healthcare, both from the perspective of the healthcare professional who wants to be able to evaluate the benefits, limitations and pitfalls of (working with) AI; as well as from the developer’s perspective, who needs to be aware of the technical background in establishing and validating algorithms for AI applications, as well as of the healthcare context.
Ready for a deeper dive into healthcare and technology? The MOOC is part of the postgraduate course 'Technologie in de zorg' by KU Leuven (only in Dutch).
What you'll learn
- Define key enabling factors and limitations of AI tools.
- Explain the main concepts of AI techniques.
- Identify added value and risks of AI in healthcare applications.
- Define healthcare data requirements, and identify related societal, ethical, and legal regulations.
- Explain the role and implications of AI in healthcare.
- Examine real-life use cases in which AI is already applied in clinical practice.
- Blood stain analysis (Philip Joris)
- Epilepsy detection (Prof. Wim Van Paesschen)
- Rheumatic Heart Disease Detection (Bart Vanrumste)
- Kidney transplant rejection classification (Thibaut Vaulet)
- Facial-based syndrome classification (Dr. Michiel Vanneste)
- Quality support in colonoscopy (Tom Eelbode and Pieter Sinonquel)
- AI-assisted surgery (Pieter De Backer, Orsi Academy)
- Hypothesis generation in psychiatry (Giovanni Briganti)
- Individual Treatment Selection (Prof. Wim Janssen and Kenneth Verstraete)
- Designing anti-microbial peptides (Alexander Koch, BioLizard)
Want to start right away? Discover these webinars as an introduction:
How can AI be used in healthcare?
AI is rapidly transforming the healthcare domain. From diagnosis to treatment: AI helps doctors and other medical professions to take better-informed decisions and to supply more personalised care.
AI in Healthcare: Hype or Help?
webinar - Skillstown & VAIA
Seizure Detection with Wearable Devices and AI
Webinar - VAIA, Flanders AI Research & KU Leuven STADIUS
Teachers / speakers
Peter Claes
Hello, I am Peter Claes, a research professor at KU Leuven. My background is in engineering, specializing in image processing and analysis, as well as human genetics. I have a passion for engineering solutions to complex problems, with a particular interest in their applications in the field of biomedicine. My journey into this field began during my PhD, where I developed a computer-based system for craniofacial reconstruction used in victim identification. Eager to bridge the gap between technology and biology in clinical settings, I embarked on a postdoctoral position at the Melbourne Dental School, University of Melbourne, Australia. This experience, and the ones after that allowed me to collaborate closely with healthcare professionals. Artificial Intelligence (AI) serves as an omnipresent tool in my work, proving indispensable in deciphering the intricacies of the data I engage with on a daily basis.
Maarten De Vos
Prof. dr. Maarten De Vos is hoogleraar aan de faculteiten Ingenieurswetenschappen en Geneeskunde van KU Leuven. Hij richt zich op het verbeteren van data science-benaderingen voor verschillende toepassingen in de gezondheidszorg. Zijn AI-oplossingen worden gebruikt op verschillende ziekenhuisafdelingen, variërend van neonatologie tot ouderenzorg.
Christos Chatzichristos
I am Christos Chatzichristos, currently a post-doctoral researcher at KU Leuven. My educational background revolves around electrical and computer engineering, with a specialization in Biomedical Applications and an emphasis on signal processing during both my Master's and Ph.D. studies. During my doctoral journey, I witnessed the profound impact of neural networks on the field of signal processing, marking the inception of my foray into the realm of AI applications. I hold a strong belief in fostering broad interdisciplinary collaborations, as I believe that research today cannot thrive in isolation within a single domain. Artificial intelligence stands as a potent tool to expedite healthcare research. However, to truly harness its potential, we must bridge the gap by facilitating healthcare professionals' understanding of fundamental AI concepts, just as they aid biomedical engineers in unraveling the mysteries of the human body. So, here's to using AI to accelerate healthcare research while ensuring that we all speak the same language – whether it's the language of algorithms or the language of anatomy!
Heidi Mertes
Heidi Mertes is professor in de medische ethiek, verbonden aan de onderzoeksgroepen Bioethics Institute Ghent en Metamedica aan de Universiteit Gent. Haar onderzoek spitst zich toe op ethische bezorgdheden in de context van medisch geassisteerde voortplanting, genetica, onderzoek op embryo’s en sinds kort ook op de impact van nieuwe technologieën in de gezondheidszorg, gaande van de evoluerende arts-patiëntrelatie tot dataveiligheid en privacy in tijden van AI en big data.
Rob Heyman
Rob Heyman is coördinator bij Kenniscentrum Data & Maatschappij.
"Hoe meer we gedigitaliseerd leven, hoe meer we gepersonaliseerde beslissingen krijgen op basis van onze informatie." Heyman heeft als doel bloot te leggen hoe deze dingen werken en mensen te laten begrijpen wat er met data gebeurt. Hij vindt het merkwaardig dat er zo weinig bekend is over data in het tijdperk van big data. Zijn methode bestaat erin het verborgen leven van gegevens bloot te leggen door deze processen in kaart te brengen in licht verteerbare teksten, scenario's en visuals. Vervolgens gebruiken hij en zijn team co-creatiesessies om de huidige praktijken in kaart te brengen met de verwachtingen van eindgebruikers, regelgevers of vernieuwers.
Related courses
Fundamental concepts of multi-omics data integration
Opleiding - Leuven - Genomics Core Leuven
Transforming Healthcare with AI
Two-day program - Cambridge - MIT Sloan School, MIT Jameel Clinic