SCOPE

CL4Health fills the gap among the different biomedical language processing workshops by providing a general venue for a broad spectrum of patient-oriented language processing research. The third workshop on patient-oriented language processing follows the successful CL4Health workshops (co-located with LREC-COLING 2024 and NAACL 2025), which clearly demonstrated the need for a computational linguistics venue focused on language related to public health.

CL4Health is concerned with the resources, computational approaches, and behavioral and socio-economic aspects of the public interactions with digital resources in search of health-related information that satisfies their information needs and guides their actions. The workshop invites papers concerning all areas of language processing focused on patients' health and health-related issues concerning the public. The issues include, but are not limited to, accessibility and trustworthiness of health information provided to the public; explainable and evidence-supported answers to consumer-health questions; accurate summarization of patients' health records at their health literacy level; understanding patients' non-informational needs through their language, and accurate and accessible interpretations of biomedical research. The topics of interest for the workshop include, but are not limited to the following:

- Health-related information needs and online behaviors of the public;
- Quality assurance and ethics considerations in language technologies and approaches applied to text and other modalities for public consumption;
- Summarization of data from electronic health records for patients;
- Detection of misinformation in consumer health-related resources and mitigation of potential harms;
- Consumer health question answering (Community Question Answering)(CQA);
- Biomedical text simplification/adaptation;
- Dialogue systems to support patients' interactions with clinicians, healthcare systems, and online resources;
- Linguistic resources, data, and tools for language technologies focusing on consumer health;
- Infrastructures and pre-trained language models for consumer health;

IMPORTANT DATES (Tentative)

February 18, 2026 -Workshop Paper Due Date️
March 13, 2026 - Notification of acceptance
March 20, 2026 - Camera-ready papers due
April 10, 2026 - Pre-recorded video due (hard deadline)
May 16, 2026 - Workshop

SHARED TASKS

Detecting Dosing Errors from Clinical Trials (CT-DEB'26).
Clinical Trials Dosing Errors Benchmark 2026 is a challenge to predict medication errors in clinical trials using Machine Learning.
The Clinical Trials Dosing Errors Benchmark 2026 (CT-DEB'26) is dedicated to automated detection of the risks of medication dosing errors within clinical trial protocols. Leveraging a curated dataset of over 29K trial records derived from the ClinicalTrials.gov registry, participants are challenged to predict the risk probabilities of protocols likely to manifest dosing errors. The dataset consists of various fields with numerical, categorical, as well as textual data types. Once the shared task is concluded and the leaderboard is published, the participants are invited to submit a paper to the CL4Health workshop
Website: https://www.codabench.org/competitions/11891/

Automatic Case Report Form (CRF) Filling from Clinical Notes.
Case Report Forms (CRFs) are standardized instruments in medical research used to collect patient data in a consistent and reliable way. They consist of a predefined list of items to be filled with patient information. Each item aims to collect a portion of information relevant for a specific clinical goal (e.g., allergies, chronicity of disease, tests results). Automating CRF filling from clinical notes would accelerate clinical research, reduce manual burden on healthcare professionals, and create structured representations that can be directly leveraged to produce accessible, patient- and practitioners-friendly summaries. Even though the healthcare community has been utilizing CRFs as a basic tool in the day-to-day clinical practice, publicly available CRF datasets are scarce, limiting the development of robust NLP systems for this task. We present this Shared Task on CRF-filling aiming to enhance research on systems that can be applied in real clinical settings.

Grounded Question Answering from Electronic Health Records.
While there have been studies on answering general health-related queries, few have focused on their own medical records. Furthermore, grounding (linking responses to specific evidence) is critical in medicine. Yet, despite extensive studies in open domains, its application in the clinical domain remains under-explored. To foster research in these sparsely studied areas of clinical natural language processing, the ArchEHR-QA (“Archer”) shared task was introduced as part of the BioNLP Workshop at ACL 2025. Given a patient-posed natural language question, the corresponding clinician-interpreted question, and the patient's clinical note excerpt, the task is to produce a natural language answer with citations to the specific note sentences. The ArchEHR-QA dataset is based on real-life patients' questions from public health forums aligned with clinical notes from publicly accessible EHR databases (MIMIC-III/IV) to form a cohesive question-answer source case. Submissions will be evaluated for evidence use (“Factuality”) and answer quality (“Relevance”). Factuality is measured via Precision, Recall, and F1 Scores between the cited evidence sentences in systems' answers and ground truth labels. Relevance is measured against ground truth answers using BLEU, ROUGE, SARI, BERTScore, AlignScore, and MEDCON.

FoodBench-QA 2026: Grounded Food & Nutrition Question Answering.
FoodBench-QA 2026 is a shared task challenging systems to answer food and nutrition questions using evidence from nutrient databases and food ontologies.The dataset includes realistic dietary queries, ingredient and their quantities lists, and recipe descriptions, requiring models to perform nutrient estimation, FSA traffic-light prediction, and food entity recognition/linking across three food semantic models. Participants must generate accurate, evidence-based answers across these subtasks (or at least one of it). After the shared task concludes and the leaderboard is released, participants will be invited to submit their work to the Shared Tasks track of the CL4Health workshop at LREC 2026.
Website: https://www.codabench.org/competitions/12112/


SUBMISSIONS

Two types of submissions are invited:

- Full papers: should not exceed eight (8) pages of text, plus unlimited references. These are intended to be reports of original research.
- Short papers: may consist of up to four (4) pages of content, plus unlimited references. Appropriate short paper topics include preliminary results, application notes, descriptions of work in progress, etc.

Electronic Submission: Submissions must be electronic and in PDF format, using the Softconf START conference management system. Submissions need to be anonymous.
The papers should follow LREC 2026 formatting.
LREC provides style files for LaTeX and Microsoft Word at https://lrec2026.info/authors-kit/.

The LREC 2026 official Overleaf template is here. It has a [review] setting that must be on for the submission. Please do not forget to turn it off for the final submission. The optional limitations and ethical considerations sections, references, and appendices should be included in the pdf for the paper (not counting towards the page limit), and not be submitted as a separate PDF.

Submission site: https://softconf.com/lrec2026/CL4Health/
Dual submission policy: papers may NOT be submitted to the workshop if they are or will be concurrently submitted to another meeting or publication.

Share your LRs: When submitting a paper from the START page, authors will be asked to provide essential information about resources (in a broad sense, i.e. also technologies, standards, evaluation kits, etc.) that have been used for the work described in the paper or are a new result of your research. Moreover, ELRA encourages all LREC authors to share the described LRs (data, tools, services, etc.) to enable their reuse and replicability of experiments (including evaluation ones).

MEETING

The workshop will be hybrid. Virtual attendees must be registered for the workshop to access the online environment.

Accepted papers will be presented as posters or oral presentations based on the reviewers’ recommendations.

ORGANIZERS
- Deepak Gupta, US National Library of Medicine
- Paul Thompson, National Centre for Text Mining and University of Manchester, UK
- Dina Demner-Fushman, US National Library of Medicine
- Sophia Ananiadou, National Centre for Text Mining and University of Manchester, UK