Artificial intelligence (AI) is often presented as a solution to major pressures facing European health systems, yet meaningful population‑level impact has been slow to emerge. This is due to several factors, including a predominantly clinical narrative that diverts attention from public health applications; a focus on productivity tools rather than the broader computational capabilities that could support system‑level action; persistent data and infrastructure barriers; unresolved ethical challenges; and a research agenda shaped largely by commercial interests. Strengthening digital and AI capability within the public health workforce is therefore essential to ensure responsible, equitable, population‑focused use of AI in health. This interactive full-day skills-building pre-conference will comprise foundational presentations, facilitated group tasks, and plenary feedback sessions to engage participants in actively building their skills in using AI technologies to address public health issues related to equity, climate change, and sustainability.

Consequently, this pre-conference will equip participants with practical tools and frameworks to critically assess AI-driven solutions to public health challenges. Importantly, it will broaden discussion beyond generative AI, introducing a range of AI approaches (e.g., predictive modelling, machine learning classification, natural language processing, computer vision, and optimisation methods) and clarifying when each is appropriate. Throughout the day, participants will be interactively guided through:

1. assessing the suitability of AI solutions in specific public health contexts;
2. considering the availability of data, infrastructure, and resources, and;
3. assessing feasibility, ethical implications, including governance, bias, and unintended consequences of AI solutions.

Objectives

• Participants will understand how AI is currently being used in public health research and practice.
• They will identify key challenges, biases, and ethical considerations in AI-driven health interventions.
• The pre-conference will foster networking and knowledge exchange between participants through interactive discussions and real-world case studies.
• The pre-conference will support participants in actively building their skills in AI for public health-related issues.
• The pre-conference will serve as a shared resource of AI use cases and best practices for equitable and sustainable implementation.

Expected outcomes

• Participants will understand the range of AI techniques relevant to public health issues surrounding equity, climate change, and sustainability.
• Participants will critically assess ethical and feasibility considerations of AI-technologies for different public health settings.
• Participants will be equipped with actionable steps for integrating AI responsibly into their public health initiatives.