General Information
| Course Code | XB_0020 |
|---|---|
| Credits | 6 EC |
| Period | P5 |
| Course Level | 200 |
| Language of Tuition | English |
| Faculty | Faculty of Science |
| Course Coordinator | prof. dr. A.C.M. ten Teije |
| Examiner | prof. dr. A.C.M. ten Teije |
| Teaching Staff |
prof. dr. A.C.M. ten Teije dr. J. Chen |
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Part of programme(s)
Bachelor
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Practical Information
You need to register for this course yourself. The (de)registration deadlines can be found on VU.nl
| Teaching Methods | Seminar, Lecture |
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Course Objective
The aim of the course is to make the students familiar with a number of Artificial Intelligence techniques (Knowledge and Understanding) and how they can be applied to the medical field (Applying Knowledge and Understanding, Making Judgments). Furthermore, students should have capability to work in teams (Communication).
Course Content
The aim of the course is to make the students familiar with a number of Artificial Intelligence techniques and how they can be applied to the medical field. The course will be structured along the patient journey: diagnosis, treatment, monitoring, and prevention. Several AI techniques will be introduced during the course. The assignments offer students the opportunity to gain practical experience with AI techniques in the health domain. In the assignments, both the AI-technique and a medical topic have to be applied in a practical, hands on, way.
The course will be structured into three thematic modules.
module 1: Diagnosis (eg. ontologies, explainable ML)
module 2: Treatment (eg. representations of guidelines, neurosymbolic approaches)
module 3: Monitoring & Prevention (eg. wearables, translational medicine)
Beside these modules there might be a couple of guest lectures, on for instance natural language processing for medical texts, bioinformatics, and ethical impact.
Additional Information Teaching Methods
There will be two lectures per week and one practical session per week.
Method of Assessment
Practical group assignments for each module, and an individual exam. Exam (50%) and practical assignments (50%) form the final grade. For both parts the grade needs to be sufficient (5.5 or higher) to obtain a final grade. No resit is possible for the practical assignments.
Literature
Selected scientific papers.Additional Information Target Audience
Bachelor Artificial Intelligence (year 2)
Additional Information
Level 2b Use of generative AI is permitted in a limited way, with disclosure
In this course, generative AI (GenAI), such as ChatGPT or similar tools, may be used in a limited way, for example for editorial support, rephrasing, or structure. Substantive contributions from GenAI may only be used within the limits set by the lecturer. Any use of GenAI must always be explicitly disclosed, in accordance with the lecturer's instructions. The student remains fully responsible for the content, quality, and accuracy of the submitted work. Incorrect use of GenAI is considered fraud. If established, the submitted work or assessment result will be declared invalid, and the Examination Board may impose further measures.
Recommended background knowledge
Recommended courses:
- Knowledge and data (X_400083)
- Machine Learning (X_400154)