A Semi-Automated Annotation Workflow for Paediatric Histopathology Reports Using Small Language Models
Electronic Patient Record (EPR) systems contain valuable clinical information, but much of it is trapped in unstructured text, limiting its use for research and decision-making. Large language models can extract such information but require substantial computational resources to run locally, and sending sensitive clinical data to cloud-based services, even when deidentified, raises significant patient privacy concerns. In this study, we develop a resource-efficient semi-automated annotation workflow using small language models (SLMs) to extract structured information from unstructured EPR data, focusing on paediatric histopathology reports. As a proof-of-concept, we apply the workflow to ...
| Authors | Avish Vijayaraghavan, Jaskaran Singh Kawatra, Sebin Sabu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 5 April 2026 |
| Last updated at source | 7 April 2026 |
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