Automated MRI pituitary structured reporting from free-text using a fine-tuned Llama model: a feasibility study.

Background And Objective: Structured reports in radiology have demonstrated substantial advantages over unstructured ones. However, the transition from unstructured to structured reporting can face challenges, as experienced radiologists worry about the potential loss of valuable information. In this study, we fine-tuned the Llama 2 model capable of generating structured pituitary MRI reports from unstructured reports.

Role of Natural Language Processing in Automatic Detection of Unexpected Findings in Radiology Reports: A Comparative Study of RoBERTa, CNN, and ChatGPT.

Rationale And Objectives: Large Language Models can capture the context of radiological reports, offering high accuracy in detecting unexpected findings. We aim to fine-tune a Robustly Optimized BERT Pretraining Approach (RoBERTa) model for the automatic detection of unexpected findings in radiology reports to assist radiologists in this relevant task. Second, we compared the performance of RoBERTa with classical convolutional neural network (CNN) and with GPT4 for this goal.

Magma-induced tectonics at the East Pacific Rise 9°50'N: Evidence from high-resolution characterization of seafloor and subseafloor.

At fast-spreading centers, faults develop within the axial summit trough (AST; 0 to 250 m around the axis) primarily by diking-induced deformation originating from the axial magma lens (AML). The formation of the prominent abyssal-hill-bounding faults beyond the axial high (>2,000 m) is typically associated with the unbending of the lithosphere as it cools and spreads away from the AST. The presence of faults is rarely mapped between these two thermally distinct zones, where the lithosphere is still too hot for the faults to be linked with the process of thermal cooling and outside of the AST where the accretional diking process dominates the ridge axis.