Berlin prepares a forum to put public-health AI under the microscope

Artificial intelligence in public health is not settled by a single software demonstration. It needs comparable data, teams able to interpret results and rules that clarify what may be done with them. That will be the ground covered by the third “Artificial Intelligence in Public Health Research” symposium, which the Robert Koch Institute (RKI) will hold in Berlin on 9 and 10 September 2026.

Bringing research and practice together

The event is organised by the RKI’s Centre for Artificial Intelligence in Public Health Research, known as ZKI-PH. The Institute’s event page says it receives financial support from Germany’s Federal Ministry of Health through the AI-DAVIS-PANDEMICS project. That matters because the discussion is not framed as computer science alone: it starts with public-health functions, where data, procedures and public trust matter as much as the model.

The previous edition helps explain the approach. The RKI says the second symposium, held in May 2025, brought together more than 180 participants and placed 15 talks in four sessions: AI-supported public-health decision-making, strategies for antimicrobial resistance, climate change and public health, and regulatory frameworks for AI and machine learning. The third meeting continues a series that began in 2023.

This is not a catalogue of applications that are ready to deploy. It is a programme of questions: how to spot disease patterns earlier without mistaking a signal for a conclusion; how to make information from different institutions usable together; and how to evaluate a tool before its outputs enter a public-health decision.

Data are not a technical footnote

The RKI’s account of the 2025 meeting highlights a repeated need: structured, standardised data. Without that foundation, comparisons across regions, population groups or time periods can produce misleading conclusions. Public-health records also reflect how measurement is done, who reaches services and which changes have occurred in a particular surveillance system.

AI can help organise very large bodies of information, identify regularities and support specialist work. But its outputs need context. A model may identify an association worth investigating; it cannot decide on its own whether that association has an epidemiological explanation, whether data are systematically missing, or whether a public intervention is proportionate.

The World Health Organization sets out that balance in its guidance on ethics and governance of AI for health. It recognises potential for health research, surveillance and outbreak response, while placing ethics and human rights at the centre of design, deployment and use. Its principles include preserving human autonomy, promoting safety and wellbeing, ensuring transparency, fostering accountability, supporting equity and evaluating systems over time.

What needs to be measured

That is why a meeting such as Berlin’s matters more for the questions it organises than for the promises it collects. Before a system enters a public routine, it is worth knowing which population it was evaluated on, the quality of its data, when it fails, who reviews its recommendations and how an outcome that harms or excludes someone can be corrected.

The symposium will bring those conversations together before AI is treated as an automatic answer. In public health, technology can extend the capacity to observe and analyse. Responsibility for interpreting, explaining and acting will remain collective and human.