Artificial intelligence and computational medicine
Postgraduate course
- ECTS credits
- 6
- Teaching semesters Spring
- Course code
- ELMED219
- Number of semesters
- 1
- Teaching language
- Norwegian (English if exchange students are enrolled in the course). The course material will be in English.
- Resources
- Schedule
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". All course material will be openly available on a GitHub repository: github.com/arvidl/ELMED219-2026ELMED219-2026">
Use of Artificial Intelligence (AI)
The use of AI tools in academic work must be guided by the principles of honesty, transparency, and academic integrity. Students are expected to follow the Faculty of Medicine’s classification system for AI use (Level G), and to clearly document their level of AI involvement in their thesis work.
Student Responsibilities
- Self-assessment: Students must assess and declare the level of AI use in their thesis, based on the official classification system.
- Critical evaluation: AI-generated content must not be used uncritically. All information must be verified, fact-checked, and integrated with academic rigor.
- Tool selection: Students are encouraged to use secure and institutionally supported AI tools. At the University of Bergen (UiB), Microsoft Copilot or UiBChat are available and considered a secure option for academic use.
For more information about the classification system and guidelines, see:
www.uib.no/en/med/174309/use-artificial-intelligence-ai-faculty-medicine
Learning Outcomes
Upon completion of the course, the student must have the following learning outcomes defined in terms of knowledge, skills and general competence:
Knowledge
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.
Skills
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
Semester of Instruction
Required Previous Knowledge
Recommended Previous Knowledge
Credit Reduction due to Course Overlap
Access to the Course
Students admitted to the Faculty of Medicine or the Faculty of Science and Technology 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 elektiv@med.uib.no. There is a deadline for signing up. Contact elektiv@med.uib.no 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
Forms of Assessment
Grading Scale
Assessment Semester
Course Evaluation
Examination Support Material
Simple, bilingual dictionary, that must be reviewable, meaning that one of the languages must be English, or a Scandinavian language.
The exam permits the use of Artificial Intelligence (AI) tools to support understanding and explanation of medical AI.
All resources are allowed, but you must specify exactly which resources you have had access to and how you used these sources (e.g., lookups, searches, prompts) in your answers.