Researchers evaluate LLMs for ILD extraction

Published in the Journal of Medical Internet Research (JMIR 2026), this comparative evaluation benchmarks large language models on extracting structured clinical variables from interstitial lung disease (ILD) patient notes. The authors note that the majority of clinically relevant data resides in unstructured clinical narratives that are verbose and imprecise, making manual or rule-based extraction costly and error-prone.

ILD is a heterogeneous group of disorders - including pulmonary fibrosis, sarcoidosis, and hypersensitivity pneumonitis - whose management depends on precise tracking of features such as disease subtype, progression markers, pulmonary function results, and radiological findings. Because these details are rarely coded in structured EHR fields and instead appear in clinician narrative, automated extraction is both high-value and technically demanding. ILD notes blend highly specific medical terminology, diagnostic reasoning, and quantitative lab values, testing the limits of general-purpose versus domain-adapted models.

Benchmarking results from this type of study inform decisions about whether to deploy general-purpose instruction-tuned models, domain-adapted clinical checkpoints, or hybrid extraction pipelines combining LLMs with structured grammars. Practitioners working on rare-disease EHR curation, registry building, or real-world evidence generation may find the methodology and error-rate patterns useful when scoping similar extraction tasks. Structured extraction quality directly affects downstream analytics: inaccurately extracted disease subtype or lung function values propagate errors into cohort definitions and outcomes research.

The JMIR paper (e90547) could not be directly fetched for independent verification during this audit. Specific model names, accuracy metrics, dataset size, and key results are not verified here beyond what is recorded in the study's indexed abstract and title.