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Students taking this course will get a topical overview of the application of a diversity of AI techniques in healthcare, and study one particular topic in detail in a literature review.
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After an introductory / overview lecture, there will be 9 to 10 content-wise lectures that together give a reasonable overview on the use of AI techniques in healthcare, corresponding to the expertise available in the AI department and in Radboud UMC. Students attend compulsory guest lectures (or send in a replacement assignment if unable to attend even online), engage in online discussion on the lecture and the accompanying reading material, and after the content-wise lectures have four weeks to focus on a topic in the course of their interest and write a literature review / summary of that area.
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A generic background in AI techniques, corresponding to a bachelor in AI or a pre-master programme giving access to the AI master, is assumed. No specific healthcare knowledge is assumed.
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The grade will be a weighted average of 1) participation in online discussion on the literature (20%) and 2) an individual literature review (%80). The literature review needs to be at least 5.5 to pass the course, and can be resit if needed. Grading for the different parts will be made public in Brightspace. Only the final grade will be published in Osiris at the end of the course.
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