Artificial intelligence and large language model

Overview: Artificial intelligence (AI) and in particular Large Language Models (LLM) are transforming research practices (e.g., automated information extraction, data cleaning, systematic review support, thematic analysis in qualitative research, assistance in analyzing algorithm).

This course will equip researchers and professionals in the field of clinical research, epidemiology and public health with the conceptual foundations needed to understand how AI systems function, evaluate their reliability, and understand how they could transform research practices.

Participants will gain a solid grounding in key concepts and terminology, as well as the ethical and regulatory considerations surrounding AI. They will also examine the practical challenges associated with real-world data and decision-making constraints. The course introduces a range of AI-based methods and equips participants with practical approaches to identify the most appropriate tools for their specific research objectives.

The course covers four key areas:

1. AI key concepts: What AI can and cannot do, model families, model evaluation, bias, uncertainty, and regulatory issues (GDPR, EU AI Act).
2. Data & Methods: Characteristics of public-health data, data quality challenges, example of tools and how to choose them.
3. Modern LLM Techniques: How LLMs work, prompting strategies, Retrieval-Augmented Generation (RAG), and agents.
4. Applied Use Cases: diagnosis, systematic review support, information extraction etc.

No coding or mathematical background is required. The course focuses on conceptual clarity and practical understanding.

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