Artificial intelligence and computational medicine

Postgraduate course

Course description

Objectives and Content

The objective and content of the course addresses
  • The computational mindset, machine learning, and AI in future medicine - pros and cons, as well as ethical and regulatory aspects of medical AI.
  • The course is a guided "journey" through selected computational modeling techniques within biomedical and clinical applications. Examples, demonstrations, and tasks will be related to in vivo imaging (MRI) and segmentation, biomarkers and prediction, network analysis ("patient similarity networks"), multimodal data, as well as large language models ("foundation models") within medicine and health. Throughout the course, students will use principles and modern tools for data analysis, machine learning, and generative AI (e.g. ChatGPT) within medical applications. This will give the students an introduction to Python and Jupyter notebooks, use of the "cloud" for access to open data, calculations, and knowledge, as well as insight into and rationale for "open science" and "reproducible research".

Learning Outcomes

Upon completion of the course, the student must have the following learning outcomes defined in terms of knowledge, skills and general competence:


The student..

  • Has broad knowledge of the terms "big data", "network analysis", "machine learning", "deep learning" and "generative AI" (large language models) and was able to relate these terms to examples from personalized and predictive medicine.


The student..

  • Can find and use a selection of modern software tools for data analysis, visualization, reporting and generative AI (e.g. data analysis, figure and graphics production with Jupyter notebooks, use of large language models such as ChatGPT).
  • Can communicate about selected methods and software where these have been implemented and explain relevance for medical research and clinical practice.

General competence

The student..

  • Recognizes the importance of mathematical models and calculations as well as large language models for the analysis and understanding of complex systems and disease processes and the need for interdisciplinary collaboration in the medicine of the future. Ethical and regulatory aspects of medical AI.
  • Can analyze how scientific collaboration in the form of "open science", sharing of data and "reproducible research" can move science forward.

ECTS Credits

6 credits (ECTS)

Semester of Instruction

First four weeks of the spring term.
Required Previous Knowledge
Preferably passed the first two study years in medical school, or first two years in engineering school within the disciplines of computer science, electrical engineering, mechanical engineering, chemical engineering, or the first two study years in the bachelor program of mathematics, informatics, physics, chemistry, or biology.
Recommended Previous Knowledge
For the medical students, recommended previous knowledge includes organ physiology, anatomy, and cell biology / molecular biology at the level of passing the second year into the medical curriculum and having curiosity/interest in technology, mathematics, and computer science. Medical students on the research track are welcome to take the course. For engineering students and students in mathematics, informatics, or physics, recommended previous knowledge includes calculus / linear algebra and computer programming at the level of the second year of engineering school or bachelor programs, together with an interest in biological and medical phenomena and applications.
Subject Overlap
BMED360/HUFY372 (2 ECTS)
Credit Reduction due to Course Overlap
Access to the Course

Students admitted to the Faculty of Medicine or the Faculty of Mathematics and Natural Sciences at UiB (or another university) and students admitted to the engineering studies at HVL (or another university/university e.g. Erasmus student). Students from outside UiB will receive guest student status upon admission to the course.

Students from outside UiB have to sign up by email to There is a deadline for signing up. Contact for information about this.

Teaching and learning methods

The teaching style is oriented towards "blended learning" and "flipped classroom":

  • ¿Two days of introductory and motivational lectures, including demonstrations. Students bring their own laptop.
  • e-learning/lab modules (before, during, and available after the course) with a focus on learning outcomes for the course will also include reflection questions and thematic multiple-choice questions.
  • A submission related to specific topics within (bio)medicine, chosen from among a small selection of pre-defined projects where collaboration between at least one medicine student and one engineering student (i.e. "tandem") is sought. This interdisciplinary group project must be presented orally at one of the four meetings.
  • Four "meet-ups" / gatherings with teachers and teaching assistants. Final digital exam.

The course will assume the students have their own laptop (or borrow one).

Compulsory Assignments and Attendance
One compulsory submission and one oral presentation, partly with ¿a pair assessment¿ component. Compulsory activities are registered by the course supervisor and must be passed before the exam.
Forms of Assessment
Final home exam (2 hours) with quiz and multiple-choice tasks
Grading Scale
Passed / failed
Assessment Semester
Course Evaluation
Written evaluation using electronic/digital evaluation tool.
Programme Committee
Programme Committee for Medicine
Course Administrator
Department of Biomedicine