COURSE
AI for Evidence Generation
Learn how AI can support evidence generation across the clinical development lifecycle. Work with literature review, real-world data and HTA, and apply AI to an evidence-related question from your own work.
Course details
København Ø
14 April 2027
Duration: 2 days
DKK 8,900.00 - 9,200.00
Prices are excluding VAT.
English
Apply AI to evidence generation in life science
AI is changing the way evidence is generated in life science. To use the technology effectively, you need to understand where AI can support your work, how it complements established approaches, and where its limitations require extra attention.
The course explores applications of AI across the clinical development lifecycle, from early research to real-world evidence. You will work with literature review, evidence synthesis, comparative effectiveness, causal real-world evidence and evidence for HTA and reimbursement submissions.
Short presentations are combined with practical exercises. You bring an evidence-related question or task from your own work and use it as a case during the course. Alternatively, you can work with a case provided by the course organiser.
Responsible use of AI is also addressed, including important limitations of current AI tools, such as fabricated references and misleading answers.
What you will work with on the course
- AI across the clinical development lifecycle, from phase I to phase IV
- The role of real-world evidence in clinical development
- GDPR and responsible use of AI
- AI for literature review and insights
- Evidence synthesis
- Comparative effectiveness and causal real-world evidence
- AI in trial design and external comparators
- AI-supported real-world evidence for HTA and reimbursement
- Limitations of AI tools, including fabricated references and misleading answers
- An evidence-related case from your own work
- Presentation and review of participant cases
This course is for you if you work with evidence generation in life science
The course is for professionals working with evidence generation in life science who want to understand how AI can support their everyday work.
It is particularly relevant if you work within:
- Clinical Development
- Medical Affairs
- Regulatory Affairs
- Market Access
- Biostatistics
- Data Science
- Epidemiology
No programming experience or advanced statistical knowledge is required
Your learning outcomes
After completing the course, you will have:
- A better understanding of where AI can support evidence generation across the clinical development lifecycle
- Experience using AI for literature review and evidence synthesis
- Knowledge of key limitations of AI tools, including fabricated references and misleading answers
- Insight into how AI can support real-world data analyses and evidence for HTA and reimbursement submissions
- Practical experience applying AI to an evidence-related question from your own work
- A stronger basis for assessing where AI adds value and where caution is required
Your organisation's benefits
Your organisation gains an employee who:
- Understands where AI can support evidence generation from phase I to phase IV
- Can use AI for literature review and evidence synthesis
- Understands important limitations of AI in evidence-related work
- Can support HTA and reimbursement submissions using AI-supported real-world evidence
- Can assess when AI is an appropriate tool for evidence-related work
Sharing of Contact Information
Atrium
This course is delivered in collaboration with Atrium. When you register for the course, your name, email address, and phone number will be shared with Atrium. The contact information will be used solely for purposes related to the administration and delivery of the course.
The UN Sustainable Development Goals are part of IDA’s values
IDA aims to highlight the connection to the UN’s 17 Sustainable Development Goals. This event addresses, among other things, the above-mentioned goals. Read more about the UN Sustainable Development Goals