Calls

Please refer to the Submission page for the detailed formats.

Call for contributions

Healthcare narrative (such as clinical notes, discharge letters, nurse handover notes, imaging reports, patients posts on social media or feedback comments, etc.) has been used as a key communication stream that contains the majority of actionable and contextualised healthcare data, but which – despite being increasingly available in a digital form – is not routinely analysed and integrated with other healthcare data on a large-scale. There are many barriers and challenges in processing healthcare free text, including, for example, the variability and implicit nature of language expressions, and difficulties in sharing training and evaluation data. On the other hand, recent years have witnessed increasing opportunities to process free text, with a number of success stories that have demonstrated the feasibility of using advanced Natural Language Processing to unlock evidence contained in free text to support clinical care, patient self-management, epidemiological research and audit.

This year, HealTAC will specifically focus on multimodal language models and human-AI interaction in clinical applications. We invite submissions in the form of extended abstracts (up to 2 pages) that describe either methodological or application work, as well as software demos, discussion panels and PhD and fellowship project submissions. The contribution types and the submission process are available here.

Topics

HealTAC 2025 invites contributions that address any aspect of healthcare text analytics. This year’s topics will focus on multimodal language models and human-AI interaction in clinical applications, but other topics are also welcome including:

Multi-modal models for healthcare decision support

(Large) language models for healthcare text analytics

Machine-learning approaches to healthcare text analytics

Transfer learning for healthcare text analytics

Speech analytics for healthcare applications

Processing clinical literature and trial reports

Text analytics and learning health systems

Healthcare ontologies and coding of healthcare text

Explainable models for healthcare NLP

Real-time processing of healthcare free text

Real-world application of healthcare text analytics

Scalable and secure healthcare NLP infrastructures

Text mining for veterinary medicine

Privacy-preserving healthcare analytics

Datasets for healthcare text analytics

Evaluation, assessment, and reproducibility in healthcare text analytics

Patient-facing text analytics: presenting clinical insights for patients

Sharing resources for healthcare text analytics (data and methods)

Information extraction: identification of clinical variables and their values in free-text

Processing patient-generated data (e.g. social media, health forums, diaries)

Implementation of healthcare text analytics in practice: public engagement and governance