Summary
AI is changing how evidence is generated in life science. The real value comes from knowing how to apply AI in your everyday work.
In the course we will explore how AI can support evidence generation across the full clinical development lifecycle, from early research to real-world evidence, and where it fits alongside the studies you already run.
The course combines short presentations with hands-on work. You bring a real question or task from your own work, or work with a case we provide
By the end of the course, you will have hands-on experience using AI in your own work and a stronger foundation for assessing where it adds value in your evidence practice, and where you need to be cautious.
Key words
- AI (Artificial Intelligence)
- Clinical and real-world evidence
- Evidence generation
- Literature review
- Target trial emulation
- HTA evaluation
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Course leader & lecturers
- Klaus Kaae AndersenCourse leaderDirector & Senior Statistician
Sanos
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Is this course for you?
- Clinical development
- Medical affairs
- Regulatory affairs
- Market access
- Biostatisticians and Data scientists
- Epidemiologists
What you will learn
- Where AI can support evidence generation across the clinical development lifecycle
- How AI can be used for literature review and evidence synthesis
- The limitations of AI tools, including fabricated references and misleading answers
- How AI can support analyses of real-world data and the development of evidence for HTA and reimbursement submissions
- How to apply AI to your own evidence-related questions
What your company will get
An employee who..
- Knows where AI supports evidence generation from phase I–IV
- Can use AI for literature review and evidence synthesis
- Knows the key limitations of using AI in evidence work
- Can strengthen HTA reimbursement submissions with AI-supported real-world evidence (Danish Medicines Council, NICE)
- Can assess when AI is a relevant tool in evidence work
Course calendar
Day 1
- AI across the clinical development lifecycle (phase I-IV) and where RWE fits
- Introduction to GDPR and responsible use of AI
- AI for literature review and insights
- AI for comparative effectiveness and causal RWE
- Working on your own case: problem, output and group discussion
Self-paced (approx. 2 hours, incl. exercises)
- AI in trial design and external comparators
- AI-supported RWE for HTA reimbursement
Day 2: (Virtual Attendance)
- Presentation and walkthrough of participants’ own cases
- Feedback
Registration
Registration deadline22 Oct 2026
Lersø Parkallé 101
2100 København Ø
9,200 DKK
Course information
Literature
Before the course you will get access to recommended readings via your personal Atrium log-in
Prerequisites
Computer with access to a large language model (LLM), such as ChatGPT, Co-pilot, Claude
Bring a real RWE question – a fictional case is available if needed.
No programming experience or advanced statistical knowledge is required.
Examination
There is no examination for this course.
Course leader
Sanos
Lecturer
Sanos
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24 Nov 2026
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Want to know more, or need help?
Contact Client Manager Laura Enemark Skyum at
+45 40 46 58 98 or lsk@atriumcph.com