If you have a subscription to ChatGPT Plus, you can also try out the Medical AI Assistant (UiBmed - ELMED219 & BMED365) and see if you can get it to answer some of your questions.
The course is offered by the Department of Biomedicine (UiB) in collaboration with the Department of Computer science, Electrical engineering and Mathematical sciences at the Western Norway University of Applied Sciences (HVL), and the Medical AI group at Mohn Medical Imaging and Visualization Center (MMIV).
The objective and content of the course address: The computational mindset, imaging, modeling, machine learning, and AI in future medicine, as well as ethical and regulatory aspects of AI.
The course, consisting of two blocks (1st block is joint with ELMED219), is a guided "journey" with a hands-on component 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, Gemini, Claude) within medical applications. The course will give the students an introduction to Python and Jupyter notebooks, use of AI-assisted coding, and the "cloud" for access to open data, calculations, and knowledge, as well as insight into and rationale for "open science" and "reproducible research".
The second block will focus on computational biomedical imaging (MRI, IMC, ...) and modelling.
Between the first and second blocks, the students will work on their project, defined by the student herself within the scope of the course.
All course material is openly available on this GitHub repository. (See also ELMED219)
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This repository contains most of the course material. Students enrolled in the course will also find some practical information at MittUiB.
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For academic questions about the course, contact course coordinator Arvid Lundervold (UiB).
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For practical or administrative inquiries, contact the Studies Section at the Department of Biomedicine at [email protected]
The content for the course is offered with a CC BY-SA 4.0 license unless otherwise stated.
(See also here)
| TIME | ACTIVITY |
|---|---|
| WEEK 1: Fri, Jan 3 |
|
| On your own | Get an overview of the course; installation of software and/or test out Google Colab |
| Follow the instructions at setup.md and MittUiB | |
| WEEK 2: Mon, Jan 6 |
|
| 10:15-14:00 BB Hist 1 |
Information About the course Motivation lectures - Computational medicine - Medical AI - SW-installation - Tools |
| Arvid and Alexander Lundervold | |
| Wed, Jan 8 | |
| 14:15-16:00 BB Hist 1 |
AI-driven innovation in healthcare & About the course project |
| Arvid and Alexander Lundervold | |
| Fri, Jan 10 | |
| 10:15-11:30 BB Hist 1 |
LAB 0: Introduction to theory and tools for machine learning |
| Alexander Lundervold | |
| 11:45-13:00 BB Hist 1 |
LAB 1: Network science and patient similarity networks (PSN) |
| Arvid Lundervold | |
| WEEK 3: Tue, Jan 14 |
|
| 09:15-13:00 BB Hist 1 |
AI-assisted innovation cont. & Python programming; recap of Lab0, Lab1 |
| Arvid and Alexander Lundervold | |
| Fri, Jan 17 | |
| 08:15-13:00 BB Hist 1 |
Lab 2: Deep learning |
| Arvid Lundervold | |
| WEEK 4: Team project |
Joint with BMED365 - Working on the course project in interdisciplinary teams (during the week) |
| Tue, Jan 21 | |
| 09:15-12:00 BB Hist 1 |
Lab 3: Generative AI and Large Language Models |
| Alexander Lundervold | |
| 13:15-16:00 BB Hist 1 |
Meet-up for team project brainstorming and coaching |
| Arvid and Alexander Lundervold | |
| WEEK 5: Tue, Jan 28 |
|
| 08:15-10:00 BB Hist 1 |
Project presentations by team (jointly with ELMED219) |
| Arvid and Alexander Lundervold | |
| Thu, Jan 30 | |
| 16:00 | Deadline for the Team Project Report - joint with ELMED219 (hand in via MittUiB) |
| Fri, Jan 31 | |
| 08:15-10:00 | Motivation lecture - Computational imaging |
| Arvid Lundervold | |
| WEEK 6: Mon, Feb 03 - Fri, Feb 07 |
Individual project during the week |
| WEEK 7: Mon, Feb 10 - Fri, Feb 14 |
Individual project during the week |
| WEEK 8: Mon, Feb 17 |
|
| 08:15-10:00 | Lab 4: Computational imaging |
| Arvid Lundervold | |
| Wed, Feb 19 | |
| 08:15-12:00 | Individual project presentation ("digital speed poster"). Upload poster (only) to MittUiB the day before |
| Fri, Feb 21 | |
| 14:15-16:00 | Motivation lecture - Computational modeling |
| Arvid Lundervold | |
| WEEK 9: Mon, Feb 24 |
|
| 08:15-10:00 | Lab 5: Computational modeling |
| Arvid Lundervold | |
| Fri, Feb 28 | |
| 08:15-10:00 | Summing up / reflections Aftermath |
| Arvid Lundervold | |
| WEEK 10: | |
| Mon, Mar 03 | Home exam: Duration: 3 hours; Assignment is handed out: 03.03.2025, 09:00; Submission deadline: 03.03.2025, 12:00; Examination system: Inspera Digital exam |
"Computational imaging, modelling and AI in biomedicine"
| Year | Link |
|---|---|
| 2024 | https://github.com/MMIV-ML/BMED365-2024 |
"Artificial intelligence and computational medicine"
"In Vivo Imaging and Physiological Modelling"
| Year | Link |
|---|---|
| 2021 | https://github.com/computational-medicine/BMED360-2021 |
| 2020 | https://github.com/computational-medicine/BMED360-2020 |