Artificial Intelligence and the Future of Scientific Congress Programming

For decades, the scientific programme of a congress has been built through a largely manual process.

Scientific committees review submitted abstracts, identify emerging research topics, group contributions into thematic sessions and structure the overall programme of the meeting. This work requires considerable time and coordination, particularly in large international congresses where hundreds or even thousands of abstracts are submitted.

Today, artificial intelligence is beginning to influence this process.

AI-based analytical tools are increasingly capable of analysing large volumes of scientific submissions, identifying thematic connections between papers and detecting emerging research patterns across different fields of study. Rather than replacing the work of scientific committees, these technologies can support decision-making by offering new ways to explore the structure of submitted content.

One of the most promising applications concerns thematic clustering.

AI systems can analyse abstracts using natural language processing techniques, identifying similarities between research topics and grouping contributions into coherent thematic clusters. This allows organisers to visualise the structure of the scientific material more clearly and to identify potential connections that might otherwise remain unnoticed.

Another important aspect concerns the identification of emerging research trends.

By analysing large numbers of submissions, AI tools can detect recurring concepts, methodological innovations or emerging research directions that may not yet be fully recognised within traditional scientific classifications. This information can help scientific committees design programmes that better reflect the evolving landscape of research.

Artificial intelligence may also help optimise the structure of congress programmes from an organisational perspective.

By analysing attendance patterns from previous editions and the thematic relevance of different sessions, AI tools can support the scheduling of presentations, helping organisers reduce overlaps between sessions addressing similar audiences and improving the overall flow of the programme.

However, the introduction of AI technologies in scientific congress programming also raises important considerations.

Scientific meetings remain fundamentally human environments where intellectual leadership, scientific judgement and peer evaluation play a central role. The responsibility for evaluating research quality and defining scientific priorities must continue to rest with the scientific community itself.

In this context, artificial intelligence should be understood not as a decision-maker but as an analytical tool that can assist scientific committees in managing increasingly complex bodies of information.

As scientific congresses continue to grow in scale and complexity, AI-driven analysis may become an important support mechanism in the design of scientific programmes. Used appropriately, these technologies could help organisers better understand the structure of emerging research landscapes while preserving the essential role of scientific judgement.