Advancing Clinical Trials with Artificial Intelligence
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| Date | Product | Location | Price | |
|---|---|---|---|---|
| 21/10/2025 | Advancing Clinical Trials with Artificial Intelligence | Brussels | EUR 1700 | Book |
| If you would like to have this course as an in-house session click here | ||||
About this course
Course schedule
- Course time: Classroom: 09h00 - 17h00
Course Description
In today’s rapidly evolving clinical research landscape, AI and Machine Learning are no longer futuristic concepts—they are transforming the way trials are designed, conducted, and analyzed. Whether you’re a clinical research professional, data scientist, or regulatory expert, staying ahead of these advancements is crucial.This one-day course will empower you with the knowledge of the growing potential of AI in clinical trials, such as optimizing efficiency and enhancing patient recruitment, while ensuring compliance with regulatory standards. Join us to gain cutting-edge insights, real-world case studies, and expert guidance on implementing AI-driven solutions in clinical research. AI and Machine Learning (ML) are revolutionizing the clinical research industry, enabling faster, more efficient, and data-driven decision-making in clinical trials. But how can these technologies be leveraged effectively while ensuring compliance and ethical integrity? This advanced course explores the full potential of AI in clinical trials, from optimizing trial design and predictive modeling to enhancing patient recruitment and real-world data utilization. Participants will gain an in-depth understanding of AI’s applications, challenges, and regulatory landscape, with real-world case studies. Key focus areas include:- AI-powered clinical trial design – improving protocol development and feasibility assessment
- Real-world data and predictive analytics – leveraging AI for more informed decision-making
- Patient recruitment and retention – using AI to identify eligible patients faster and improve engagement
- Regulatory and ethical considerations – understanding compliance requirements from global regulatory bodies (FDA, EMA, etc.)
- Implementing AI in clinical workflows – overcoming adoption challenges and maximizing efficiency
Programme highlight
- AI & Machine Learning: Key concepts and emerging applications
- Enhancing Trial Design & Data Analysis: The role of AI in improving efficiency
- Optimizing Patient Recruitment & Retention: AI-driven strategies for better participant selection
- Regulatory & Ethical Considerations: Understanding compliance and risk management
- Implementing AI in Clinical Research: Practical insights on integrating AI tools.
Learning objectives
By the end of this course, participants will:
- Gain a foundational understanding of AI and ML in clinical trials
- Explore how AI can support trial design, patient recruitment, and data analysis
- Understand key regulatory and ethical considerations for AI-driven research
- Learn practical approaches for integrating AI into clinical trial workflows
Who should attend
This course is ideal for professionals involved in clinical research who want to stay ahead in an AI-driven industry, including:
- Clinical Research Professionals: Clinical Research Associates (CRAs), Clinical Trial Managers, Clinical Project Leaders
- Data Science & Medical Analytics Experts: Biostatisticians, Data Managers, AI/ML Developers in Healthcare
- Regulatory & Compliance Specialists: Regulatory Affairs Professionals, Quality Assurance Experts
- Pharmaceutical & Biotech Leaders: Medical Affairs Professionals, R&D Scientists, Innovation & Digital Transformation Leads
Competencies
This course covers competencies that are part of the ECCRT Competency Framework.
- Scientific Concepts & Research Design ()
- Ethical & Participants Safety Considerations ()
- Investigational Product Development and Regulation ()
- Clinical Studies Operations (GCPs or ISO 14155) ()
- Study and Site Management ()
- Data Management and Informatics ()
- Leadership and Professionalism ()
- Communication ()
- Teamwork ()
- Business acumen ()
Trainers
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