BioASQ hosts 14th biomedical language tasks with 87 teams competing
Overview of BioASQ 2026: The fourteenth BioASQ Challenge on Large-Scale Biomedical Semantic Indexing and Question Answering
Computation and LanguageArtificial IntelligenceInformation Retrieval
Summary
Biomedical language processing helps computers understand medical texts and answer questions. The BioASQ challenge brings together teams worldwide to build and test systems for tasks like question answering, summarizing clinical notes, and extracting medical information in different languages. In 2026, the challenge featured six different tasks and had 87 teams participate, showing continued advances in this field. The authors provide an overview of these efforts and the progress made.
What this means in practice
- •For clinical informatics teams: Use evaluated biomedical NLP systems to improve automated coding, summarization, and question answering on patient data in hospitals.
- •For healthcare ai developers: Develop and benchmark medical question answering tools and information extraction solutions using BioASQ task datasets for more accurate clinical AI products.
Authors
Anastasios Nentidis, Georgios Katsimpras, Anastasia Krithara, Martin Krallinger, Miguel Rodríguez-Ortega, Eduard Rodriguez-López, Natalia Loukachevitch, Igor Rozhkov, Elena Tutubalina, Dimitris Dimitriadis, Vasiliki Patsiou, Grigorios Tsoumakas, George Giannakoulas, Alexandra Bekiaridou, Athanasios Samaras, Giorgio Maria Di Nunzio, Nicola Ferro, Stefano Marchesin, Marco Martinelli, Gianmaria Silvello, Georgios Paliouras
Abstract
This paper presents an overview of the fourteenth edition of the BioASQ challenge, organized in the context of the Conference and Labs of the Evaluation Forum (CLEF) 2026. BioASQ is an international challenge series that supports progress in biomedical language processing tasks ranging from semantic indexing and information extraction to question answering and summarization. In 2026, BioASQ included six shared tasks: a) Task 14b on biomedical semantic question answering. b) Task Synergy14 on question answering for developing biomedical top- ics. c) Task MultiClinSum-2 on multilingual clinical summarization. d) Task BioNNE-R on extracting relations between nested named entities in Russian and English. e) Task ELCardioCC on clinical coding in cardiology. f) Task GutBrainIE on gut-brain interplay information extrac- tion. Across these six tasks, 87 distinct teams participated, submitting more than 1000 runs overall. As in previous editions, several submissions reached competitive performance, reflecting the continued progress of state-of-the-art methods across biomedical language processing tasks.