Poikavila Ullaskrishnan

Ali H. Dhanaliwala, Rikhiya Ghosh, Sanjeev Kumar Karn et al.

Radiologists produce unstructured data that can be valuable for clinical care when consumed by information systems. However, variability in style limits usage. Study compares system using domain-adapted language model (RadLing) and general-purpose LLM (GPT-4) in extracting relevant features from chest radiology reports and standardizing them to common data elements (CDEs). Three radiologists annotated a retrospective dataset of 1399 chest XR reports (900 training, 499 test) and mapped to 44 pre-selected relevant CDEs. GPT-4 system was prompted with report, feature set, value set, and dynamic few-shots to extract values and map to CDEs. Output key:value pairs were compared to reference standard at both stages and an identical match was considered TP. F1 score for extraction was 97% for RadLing-based system and 78% for GPT-4 system. F1 score for mapping was 98% for RadLing and 94% for GPT-4; difference was statistically significant (P<.001). RadLing's domain-adapted embeddings were better in feature extraction and its light-weight mapper had better f1 score in CDE assignment. RadLing system also demonstrated higher capabilities in differentiating between absent (99% vs 64%) and unspecified (99% vs 89%). RadLing system's domain-adapted embeddings helped improve performance of GPT-4 system to 92% by giving more relevant few-shot prompts. RadLing system offers operational advantages including local deployment and reduced runtime costs.

Youngjin Yoo, Bogdan Georgescu, Yanbo Zhang et al.

Recent advancements in AI and medical imaging offer transformative potential in emergency head CT interpretation for reducing assessment times and improving accuracy in the face of an increasing request of such scans and a global shortage in radiologists. This study introduces a 3D foundation model for detecting diverse neuro-trauma findings with high accuracy and efficiency. Using large language models (LLMs) for automatic labeling, we generated comprehensive multi-label annotations for critical conditions. Our approach involved pretraining neural networks for hemorrhage subtype segmentation and brain anatomy parcellation, which were integrated into a pretrained comprehensive neuro-trauma detection network through multimodal fine-tuning. Performance evaluation against expert annotations and comparison with CT-CLIP demonstrated strong triage accuracy across major neuro-trauma findings, such as hemorrhage and midline shift, as well as less frequent critical conditions such as cerebral edema and arterial hyperdensity. The integration of neuro-specific features significantly enhanced diagnostic capabilities, achieving an average AUC of 0.861 for 16 neuro-trauma conditions. This work advances foundation models in medical imaging, serving as a benchmark for future AI-assisted neuro-trauma diagnostics in emergency radiology.