Artificial Intelligence (AI) in Medicine
Full course description
Outline of the programme
In this 4 week course, students will familiarize themselves with the uses of AI in medicine / healthcare both from a theoretical and practical perspective.
The first 2 weeks of the course will take place at Maastricht University where students will learn to effectively cooperate in small groups with persons of different background on a number of principles and pitfalls of AI in medicine, both in theory and in practice. The second 2 weeks of the course will take place at the University of Paris where students will put into practice what they have learned in the first 2 week at Maastricht University and where they will further specialise in the field of AI in medicine.
The different topics will contain one or more of the following educational formats:
- PBL cases
- interactive lectures
- practicals
- visit to imaging and radiotherapy facilities that use AI
- seminars
International health themes
The course is well suited for international students, as students will be able to experience the use of AI in medicine in two different countries (The Netherlands and France) and collaboration between students from different countries is required.
Required knowledge
No prior knowledge is required; good command of English language is important, some programming experience and interest in artificial intelligence in medicine is recommended.
Feedback
Weekly feedback will be provided on the ‘working out’ of and participation in the cases, practicals and interactive lectures by staff and peer students. Feedback will be assembled by the students in a short personal portfolio. Halfway and at the end of the course a feedback and evaluation session are scheduled to review the course and prepare for the final group presentation and group report.
Course objectives
In this module, students will familiarize themselves with the basics of AI; from the underlying mechanisms to an overview of the current state. Furthermore, they will explore the issues that influence (individual) uptake of AI by stakeholder (groups) in the context of health care and prevention, both from a theoretical and practical perspective. Ideally, healthcare practitioners will understand the technical, legal, and ethical challenges facing clinical AI use.
Knowledge and insights
After completing the module, the student has knowledge of:
- - the underlying mechanisms of AI and machine learning;
- - the fundamentals of data curation for the purpose of training AI and machine learning
- - the technical, legal, and ethical challenges facing clinical AI use
Application of knowledge and understanding
After completing the module, the student is able to:
- read and modify computer code in the language Python
- identify aspects of datasets that need to be curated for the purpose of training AI and machine learning, and to curate these.
Forming Opinions
After completing the module, the student is able to:
- judge the quality of scientific publications and reviews regarding the application of AI in a healthcare setting
Communication
After completing the module, the student is able to:
- have a clear view of the contributions of AI to the field of medicine and communicate this.
- communicate the underlying mechanisms of AI and machine learning in oral and written form
Learning skills
After completing the module, the student is able to:
- effectively cooperate in small groups with persons of different background and initial level of experience with AI
Recommended reading
Smith 2021 and Lin SY, Mahoney MR, Sinsky CA. Ten ways artificial intelligence will transform primary care. J Gen Intern Med. 2019;34:1626-1630.