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MultiClinAI Shared Task Homepage<\/strong><\/h1>\n\n\n

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The MultiClinAI Track is organized by the Barcelona Supercomputing Center\u2019s NLP for Biomedical Information Analysis group<\/em> and promoted<\/em> by European projects such as DataTools4Heart and AI4HF.<\/em><\/em><\/p>\n\n\n\n

What is MultiClinAI?<\/h2>\n\n\n\n

MultiClinAI<\/strong> is a shared task focused on the creation of comparable multilingual corpora via annotation projection, as well as the multilingual extraction of clinical concepts.<\/p>\n\n\n\n

For more information about the MultiClinAI<\/strong> task, check the Task Info<\/strong><\/a> tab, which includes the Motivation<\/strong><\/a>, Subtasks<\/a><\/strong>, Schedule<\/a><\/strong> and Registration<\/a><\/strong>, as well as the Evaluation & Submission<\/strong> tab.<\/p>\n\n\n\n

To learn more about the MultiClinAI corpora and how they were annotated, check the Data<\/a><\/strong> tab.<\/p>\n\n\n\n

MultiClinAI will be held as part of the #SMM4H-HeaRD Workshop<\/strong> in the ACL 2026<\/strong> conference. For more information about them, check the Workshop<\/strong><\/a> tab.<\/p>\n\n\n\n

MultiClinAI is organized by the Barcelona Supercomputing Center’s NLP for Biomedical Information Analysis group (formerly Text Mining Unit).<\/p>\n\n\n\n

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Motivation<\/h2>\n\n\n\n

Named entity recognition (NER) systems are fundamental for clinical natural language processing (NLP), enabling the identification of key clinical concepts\u2014such as diseases, symptoms, medications, and procedures\u2014in medical documents and electronic health records (EHRs). These systems support clinical workflows and decision-making, as well as large-scale health data analysis. However, their development relies on high-quality expert-annotated corpora, which are costly, time-consuming to produce, and typically language-specific. This poses significant challenges in multilingual settings, particularly for low-resource languages (LRLs).<\/p>\n\n\n\n

Multinational clinical studies, rare disease research, and multicentric trials require comparable annotation criteria and interoperable information extraction systems across languages. Yet, multilingual clinical corpora annotated under consistent guidelines remain scarce. Recent advances in machine translation, large language models (LLMs), and generative AI provide new opportunities to translate annotated datasets, project annotations across languages, and create comparable multilingual corpora through annotation projection and entity alignment strategies.<\/p>\n\n\n\n

In this context, the MultiClinAI<\/strong> (Multilingual Clinical Entity Annotation Projection and Extraction) shared task addresses the creation and evaluation of comparable multilingual clinical resources across seven languages (Czech<\/strong>, English<\/strong>, Spanish<\/strong>, Dutch<\/strong>, Italian<\/strong>, Romanian <\/strong>and Swedish<\/strong>), focusing on three key entity types: diseases, symptoms, and procedures.<\/p>\n\n\n\n

By jointly evaluating multilingual extraction performance and annotation projection strategies across seven languages, MultiClinAI<\/strong> establishes a robust benchmarking scenario for multilingual clinical NLP. The shared task encourages the development of generalizable, transferable, and scalable approaches capable of supporting cross-lingual healthcare applications.<\/p>\n\n\n\n

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Task Overview<\/h2>\n\n\n\n

MultiClinAI<\/strong> is divided into two independent but complementary subtasks, as described below:<\/p>\n\n\n\n