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. M.L. De Paula Bueno
Other course modules lecturer
dr. M.L. De Paula Bueno
Other course modules lecturer
Contactperson for the course
dr. M.L. De Paula Bueno
Other course modules lecturer
prof. dr. ir. J.H.P. Kwisthout
Other course modules lecturer
Academic year2023
SEM1  (04/09/2023 to 26/01/2024)
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-
By the end of the course, you will have studied one topic of your choice related to AI in detail in a literature review assignment. In the literature review assignment, the student will:
  • formulate a clear thesis statement in the field of AI for Healthcare that aims to be original and creative;
  • can select relevant scientific literature directly related to the thesis statement;
  • can organize the pertinent literature so as to provide a convincing line of reasoning from the thesis statement to the conclusion in the literature review;
  • can summarize the main messages of experimental, theoretical, and review papers related to AI for Healthcare in a coherent and reflective way;
  • can deliver a well-formatted and grammatically correct review paper written in English (academic writing skills).
In this course, you will get a topical overview of the application of a diversity of AI techniques in healthcare. After an introductory / overview lecture, there will be 9 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. For example, the state of the art AI in radiology, as well as its limitations, will be explained by an expert in the field. Each week, you will pose a question or discussion point on each week’s topic and reply to other students’ questions and comments based on the suggested reading and the guest lecture.

The guest lectures are mandatory (or you must send in a replacement assignment if unable to attend even online). In addition to the guest lectures, you are expected to engage in online discussions on each lecture and the accompanying reading material. After the content-wise lectures, you have an additional four weeks to focus on a topic of your 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.
Please sign up for any course at (, it is obligatory.

Students who are enrolled for a course are also provisionally registered for the exam. 

Resit: Manual register at ( until five working days prior to the date of the exam. No delayed registration is possible. 

We urge you to always read the course information on Brightspace. 
Required materials
Several scientific articles will be made available.

Instructional modes
Attendance MandatoryYes

Test weight1
Test typeEssay
OpportunitiesBlock SEM1, Block SEM2