This course explores the transformative role of artificial intelligence in healthcare. Students will examine current and emerging applications of artificial intelligence (AI) tools in research, diagnostic, and treatment processes aimed at improving patient care, as well as AI's administrative potential to reduce provider burden related to paperwork and patient record management. Drawing extensively on Danish and European examples, students will critically engage with the opportunities and ethical challenges of AI adoption, including issues of privacy, data security, algorithmic bias, and patient safety. Through case studies and structured discussion, students will develop a nuanced, evidence-based understanding of both the opportunities and challenges AI presents for healthcare.

No prior technical or computer science background is required. The course approaches AI conceptually and applied to healthcare contexts, not as a technical/programming course. 

Learning Objectives (tentative)

• Explain current and emerging applications of AI across the healthcare related field, including research and drug discovery, diagnostics, treatment planning, and administrative/clinical workflow.
• Critically evaluate real-world case studies of AI implementation in healthcare systems.
• Analyze the ethical, legal, and regulatory challenges raised by AI in healthcare, including patient privacy, data security, algorithmic bias, and patient safety.
• Assess how AI-driven administrative and decision-support tools affect workload, clinical judgment, and the patient-doctor relationship.
• Formulate and defend an evidence-informed position on the opportunities and risks of AI adoption in healthcare.

Course Modules (tentative)

• Module 1 – Introduction to potential and limitations of AI use in clinical care
• Module 2 – AI in Diagnostics and Clinical Decision-Making
• Module 3 – AI in Research and Drug Discovery
• Module 4 – AI and the Administrative Burden
• Module 5 – Ethics, Law, and Regulation

Field Studies

There are 2 field studies associated with this course. 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 and debates, critical analysis of reading material, student presentations, and field studies.

The pace and specific activities planned for certain days may change depending on the interest of the students.

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.

Shown below, a tentative overview of the factors influencing the final grade and the proportional importance of each factor: