LOG797 Artificial Intelligence and Data-Driven Improvement in the Healthcare Sector (Spring 2027)

This course will introduce non-technical students to the world of applied artificial intelligence (AI) and empower them to understand and apply AI concepts to real-world problems. Through a blend of theoretical explanations, practical hands-on exercises, and engaging case studies, students will gain a comprehensive understanding of how AI is transforming various industries and will develop the skills to harness its power in their own careers or endeavours.

This course is designed for non-technical students, including those from business, humanities, and social sciences, who are interested in understanding and applying AI concepts in their fields. It is also suitable for individuals seeking to gain a broader understanding of AI and its potential impact on society and daily work tasks.

Prerequisites: None

Overview: A hands-on introduction for non-technical health professionals. Learn what AI is, how it works, and how to use it responsibly to analyze data, streamline workflows, and support everyday decisions. By the end, you'll know where AI fits in clinical and administrative routines—and how to get practical value from it.

Knowledge

The student should have advanced knowledge within:

• • • The concepts of AI and its impact on society and daily work tasks.
    • Recognize the applications of AI across various domains, including healthcare, logistics, finance, marketing, and education.
    • The ethical considerations and responsible use of AI in real-world settings. Responsible AI principles.
    • Apply AI tools and techniques to solve practical problems using Python, an accessible programming language.
    • Use and understand generative AI tools
    • Develop critical thinking and problem-solving skills through a series of hands-on projects

Skills

The student is able to:

• analyze and deal critically with various sources of information
• analyze existing theories and concepts using big data and AI
• work independently on practical and theoretical problems.
• can analyze and interpret AI and data driven concepts on a practical level

General competence

After completing the course, the student can:

• • • • analyze relevant academic, professional and research ethical problems connected to AI and data driven decisions.
      • apply his/her knowledge and skills in new areas in order to carry out advanced assignments and projects
      • can communicate extensive independent work connected to AI and data driven healthcare.
      • can communicate about academic issues, analyzes and conclusions in the field, with specialists
      • contribute to new thinking and innovation processes

Lectures and workshops. All teaching and practice will take place digitally. Some lectures are mandatory.

Assessment & Weighting

• Weekly assignments - 40%

  • Complete at least 75% of the assigned exercises.
  • Grading: Letter scale AF.

• Applied project - 60%

  • Evaluated via a written report describing the applied work performed.
  • Grading: Letter scale AF.