AI for Healthcare
Course infoSchedule
Course moduleSOW-MKI72
Credits (ECTS)6
CategoryMA (Master)
Language of instructionEnglish
Offered byRadboud University; Faculty of Social Sciences; Artificial Intelligence;
dr. O. Colizoli
Other course modules lecturer
Contactperson for the course
dr. O. Colizoli
Other course modules lecturer
dr. J.H.P. Kwisthout
Other course modules lecturer
Academic year2021
SEM1  (06/09/2021 to 28/01/2022)
Starting block
Course mode
Please note: if you do not yet have a master's registration, you are not yet registered for the tests for this course.
Registration using OSIRISYes
Course open to students from other facultiesYes
Waiting listNo
Placement procedure-
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.
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.
Presumed foreknowledge
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.
Test information
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.
Required materials
Several scientific articles will be made available.

Instructional modes
Attendance MandatoryYes

Test weight1
Test typeEssay
OpportunitiesBlock SEM1, Block SEM2