Identifying interstitial lung disease with large language models on clinical notes and claims

Interstitial lung disease (ILD) is a potentially fatal toxicity of immune checkpoint inhibitors and antibody-drug conjugates. Drug-safety studies in healthcare databases identify ILD with claims-based algorithms that trade positive predictive value (PPV) against sensitivity. We assessed whether large language models (LLMs) improve on these algorithms, with and without electronic health record (EHR) notes, and whether these gains translate into measurable epidemiological benefits. In 309 oncology patients with ILD diagnosis codes at Mass General Brigham, adjudicated by two pulmonologists, we co

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