Automated decision making in Barrett's oesophagus: development and deployment of a natural language processing tool.

Manual decisions regarding the timing of surveillance endoscopy for premalignant Barrett's oesophagus (BO) is error-prone. This leads to inefficient resource usage and safety risks. To automate decision-making, we fine-tuned Bidirectional Encoder Representations from Transformers (BERT) models to categorize BO length (EndoBERT) and worst histopathological grade (PathBERT) on 4,831 endoscopy and 4,581 pathology reports from Guy's and St Thomas' Hospital (GSTT).