SOW-MKI72
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;
Lecturer(s)
Coordinator
dr. O. Colizoli
Other course modules lecturer
Contactperson for the course
dr. O. Colizoli
Other course modules lecturer
Examiner
dr. J.H.P. Kwisthout
Other course modules lecturer
Academic year2021
Period
SEM1  (06/09/2021 to 28/01/2022)
Starting block
SEM1
Course mode
full-time
Remark
Please note: if you do not yet have a master's registration, you are not yet registered for the tests for this course.
Remarks-
Registration using OSIRISYes
Course open to students from other facultiesYes
Pre-registrationNo
Waiting listNo
Placement procedure-
Aims
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.
Content
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
Articles
Several scientific articles will be made available.

Instructional modes
Lecture
Attendance MandatoryYes

Tests
Essays
Test weight1
Test typeEssay
OpportunitiesBlock SEM1, Block SEM2