Doctors Identify Hemorrhage Better during Chart Review when Assisted by Artificial Intelligence.

Objectives: This study evaluated if medical doctors could identify more hemorrhage events during chart review in a clinical setting when assisted by an artificial intelligence (AI) model and medical doctors' perception of using the AI model.

Methods: To develop the AI model, sentences from 900 electronic health records were labeled as positive or negative for hemorrhage and categorized into one of 12 anatomical locations. The AI model was evaluated on a test cohort consisting of 566 admissions.

Deep learning detects and visualizes bleeding events in electronic health records.

Background: Bleeding is associated with a significantly increased morbidity and mortality. Bleeding events are often described in the unstructured text of electronic health records, which makes them difficult to identify by manual inspection.

Objectives: To develop a deep learning model that detects and visualizes bleeding events in electronic health records.

Neural networks for automatic scoring of arthritis disease activity on ultrasound images.

Background: The development of standardised methods for ultrasound (US) scanning and evaluation of synovitis activity by the OMERACT-EULAR Synovitis Scoring (OESS) system is a major step forward in the use of US in the diagnosis and monitoring of patients with inflammatory arthritis. The variation in interpretation of disease activity on US images can affect diagnosis, treatment and outcomes in clinical trials. We, therefore, set out to investigate if we could utilise neural network architecture for the interpretation of disease activity on Doppler US images, using the OESS scoring system.