Performance of Multiple Pretrained BERT Models to Automate and Accelerate Data Annotation for Large Datasets.

Purpose: To develop and evaluate domain-specific and pretrained bidirectional encoder representations from transformers (BERT) models in a transfer learning task on varying training dataset sizes to annotate a larger overall dataset.

Materials And Methods: The authors retrospectively reviewed 69 095 anonymized adult chest radiograph reports (reports dated April 2020-March 2021). From the overall cohort, 1004 reports were randomly selected and labeled for the presence or absence of each of the following devices: endotracheal tube (ETT), enterogastric tube (NGT, or Dobhoff tube), central venous catheter (CVC), and Swan-Ganz catheter (SGC).

Facilitating Surveillance of Incidental Findings Using a Novel Reporting Template: Proof of Concept in Patients With Pancreatic Abnormalities.

Objective: To determine the surveillance impact of utilizing a discrete field in structured radiology reports in patients with incidental pancreatic findings.

Methods: We implemented a dictation template containing a discrete structured field element to auto-trigger listing of patients with incidental pancreatic findings on a pancreas clinic registry in the electronic health record. We isolated CT and MRI reports with incidental pancreatic findings over a 24-month period.