Related Experiment Video
Updated: May 24, 2026
Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
Published on: June 10, 2025
Describing Data Processing in FHIR: AI-Assisted Interoperability for Cancer Stage Extraction
David Ouagne1, Vincent Zossou1, Bastien Rance1,2
1AP-HP, Paris, France.
Introduction:
Creating interoperable clinical data models in FHIR is essential but often labor-intensive. This study explores the use of Generative AI to assist in documenting and structuring FHIR-based data transformation workflows, focusing on TNM cancer staging extraction.
Methods:
Using FHIR Release 4 and the PlanDefinition resource, we modeled transformation processes defined through Business Process Model and Notation (BPMN). The large language model Claude Code (Sonnet 4.5) generated FHIR artifacts from BPMN inputs, which were validated through syntax checks, Implementation Guide compilation, and expert review.
Results:
AI-assisted generation produced a validated PlanDefinition with seven structured activities, accurately representing the TNM extraction workflow. Artifacts were interoperable and passed all FHIR conformance tests after minor revisions. Conclusion / Discussion: Generative AI can effectively support FHIR workflow modeling, reducing complexity and development time. However, expert validation remains essential to ensure semantic accuracy and reproducibility.Abstract goes here.
More Related Videos
05:33Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
Published on: July 11, 2025
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018