The consensus-based CINEX guideline for reporting clinical information extraction studies
Year: 2026
Type: article
Abstract: OBJECTIVE: Information extraction (IE) from clinical texts has advanced rapidly with recent advances in natural language processing, particularly the advent of large language models (LLMs). However, inconsistent and incomplete reporting of methodologies limits reproducibility, comparability, and clinical translation. We aimed to develop a consensus-based reporting guideline tailored to clinical IE studies. MATERIAL AND METHODS: We developed the Clinical Information Extraction Reporting Guideline (CINEX) through a multi-phase process. The initiative was prospectively registered on the EQUATOR Network as a reporting guideline under development, and a detailed Delphi study protocol was published in advance. A scoping review informed an initial set of items, which was refined through a 3-round electronic Delphi study with 20 international experts, followed by a final consensus meeting. Items were iteratively refined based on predefined inclusion criteria and expert feedback. RESULTS: The final CINEX guideline comprises 29 checklist items grouped into 5 domains: information model, architecture, data, annotation, and outcomes. The 3 eDelphi rounds included 20, 15, and 12 experts, respectively. Two items were added after round one. Consensus for inclusion was reached for a 21 and additional 7 items after rounds 2 and 3, respectively. DISCUSSION: CINEX provides a structured framework to improve transparency, reproducibility, and interpretability in clinical IE research. By standardizing reporting of key methodological components, such as data provenance, annotation processes, and evaluation strategies, it facilitates meaningful comparison across studies and supports safer clinical implementation. CONCLUSION: CINEX complements existing AI reporting standards by addressing domain-specific challenges in clinical IE.
Institutions: University of Geneva, Bern University of Applied Sciences, University Hospital of Geneva, Inserm, Laboratoire Traitement du Signal et de l'Image, Centre Hospitalier Universitaire de Martinique, Centre Hospitalier Universitaire de Rennes, Université de Rennes, University of Zurich, University of Bern, University Hospital of Bern, University of Applied Sciences and Arts of Southern Switzerland, HES-SO Valais-Wallis
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $4,332
Funder: Berner Fachhochschule
OpenAlex ID: W7201985853