The course is broken down into five main parts (short description of the main topics covered in the course):

1. AI, possibilities, and challenges in healthcare:
This part will cover the basics of AI/ML including supervised and unsupervised techniques in one lecture. Then in the second lecture, possibilities and challenges of what AI can bring into healthcare will be discussed, how XAI can mitigate them, and how information-driven care can transform healthcare.

2. Information driven use cases in healthcare:
This part will cover the current main areas of application, i.e.; i) medical related use cases including diagnostics, triaging, and treatment and ii) management related use cases including procedure- and arrival analysis and patient profiles.

3. Hands-on AI workshop:
this part will introduce Python programming and how AI/ML solutions are developed. Several pre-written exercises using real-world health datasets will be provided as a solution within the smart healthcare domain. Students will have the opportunity to try the provided exercises in different scenarios, manipulate them and get the feeling of how ML development is performed in practice.

4. Relevant Regulations in Healthcare:
This part will briefly review relevant regulations including Medical Device Regulation, GDPR, CE-marking, ethical approval etc.

5. Data sources in Information driven healthcare:
In this part, existing data sources will be covered, including EHR, HR systems, national quality registers and other. An introduction to the ongoing work of centralized data storage with common API’s will be given, including a discussion on pros and cons.