Course Syllabus

This course examines how artificial intelligence is applied within healthcare and biomedical research. It covers core concepts in medical AI and explores applications in medical imaging, diagnostics, prognostics, and drug discovery. Students engage with clinical and HumanLab datasets to understand how models identify patterns, predict outcomes, and support decision making. The course also introduces AI driven tools for drug discovery and discusses challenges related to data quality, bias, fairness, and regulatory frameworks. Emphasis is placed on linking algorithmic methods to real clinical and biomedical contexts.  

Tentative Course Modules

• Coming soon

Learning Objectives

The aim of the course is to provide an overview of the applicability and use of ML/AI (Machine Learning/Artificial Intelligence) in Life Sciences with the intention to deepen the student's understanding of the use of ML/AI in image analysis, drug discovery, diagnostics and prognostics.

 After completing the course, the student shall be able to:

• Describe core ML and AI concepts and their role in Life Science
• Gain an overview of ML/AI platforms and complete a guided hands-on exercise
• Discuss the implications of the use of ML/AI in drug discovery, image analysis, diagnostics and prognostics
• Have a critical and ethical approach to the use of ML/AI in Life Sciences
• Describe core concepts in machine learning, deep learning, and large language models
• Describe how AI tools like AlphaFold accelerate drug discovery and protein structure prediction
• Show practical skills in handling and analyzing clinical datasets and medical images
• Describe AI's potential role in analyzing clinical datasets from HumanLab and similar human physiology experiments
• Discuss major ethical, regulatory, and safety considerations in medical AI
• Critically evaluate AI models’ reliability, fairness, and clinical relevance

Field Studies

Potential field studies may include visits to clinical and research laboratories.

Approach to Teaching

You will work both individually and in teams. We will use various teaching methods, including interactive lectures, class discussions, critical analysis of reading material, and field studies, hands-on experiences, and student presentations.  

The course includes theoretical and practical aspects of the use of ML/AI in Life Sciences.

Expectations of the Students

• Students should participate during lectures, peer-led oral presentations, discussions, group work and exercises.
• Laptops may be used for note‐taking, fact‐checking, or assignments in the classroom, but only when indicated by the instructor. At all other times laptops and electronic devices should be put away during class time.
• Reading must be done prior to the class session. A considerable part of the class depends on class discussions.
• Students need to be present and participate to receive full credit. The final grade will be affected by unexcused absences and lack of participation. Remember to be in class on time!
• Classroom etiquette includes being respectful of other opinions, listening to others and entering a dialogue in a constructive manner.
• Students are expected to ask relevant questions in regards to the material covered.

Evaluation and Grading 

To be eligible for a passing grade in this class, all of the assigned work must be completed.

Students are expected to turn in all the assignments on the due date. If an assignment is turned in after the due date, the grade of the assignment will be reduced by 10 points (over 100) for each day the submission is late.

The factors influencing the final grade and the proportional importance of each factor is shown below: