  Radiologists produce unstructured data that could be valuable for clinical
care when consumed by information systems. However, variability in style limits
usage. Study compares performance of system using domain-adapted language model
(RadLing) and general-purpose large language model (GPT-4) in extracting common
data elements (CDE) from thoracic radiology reports. Three radiologists
annotated a retrospective dataset of 1300 thoracic reports (900 training, 400
test) and mapped to 21 pre-selected relevant CDEs. RadLing was used to generate
embeddings for sentences and identify CDEs using cosine-similarity, which were
mapped to values using light-weight mapper. GPT-4 system used OpenAI's
general-purpose embeddings to identify relevant CDEs and used GPT-4 to map to
values. The output CDE:value pairs were compared to the reference standard; an
identical match was considered true positive. Precision (positive predictive
value) was 96% (2700/2824) for RadLing and 99% (2034/2047) for GPT-4. Recall
(sensitivity) was 94% (2700/2876) for RadLing and 70% (2034/2887) for GPT-4;
the difference was statistically significant (P<.001). RadLing's domain-adapted
embeddings were more sensitive in CDE identification (95% vs 71%) and its
light-weight mapper had comparable precision in value assignment (95.4% vs
95.0%). RadLing system exhibited higher performance than GPT-4 system in
extracting CDEs from radiology reports. RadLing system's domain-adapted
embeddings outperform general-purpose embeddings from OpenAI in CDE
identification and its light-weight value mapper achieves comparable precision
to large GPT-4. RadLing system offers operational advantages including local
deployment and reduced runtime costs. Domain-adapted RadLing system surpasses
GPT-4 system in extracting common data elements from radiology reports, while
providing benefits of local deployment and lower costs.

Computation and Language;Image and Video Processing