Visual-textual integration in LLMs for medical diagnosis: A preliminary quantitative analysis.

Background And Aim: Visual data from images is essential for many medical diagnoses. This study evaluates the performance of multimodal Large Language Models (LLMs) in integrating textual and visual information for diagnostic purposes.

Methods: We tested GPT-4o and Claude Sonnet 3.

NutriSighT: Interpretable Transformer Model for Dynamic Prediction of Hypocaloric Enteral Nutrition in Mechanically Ventilated Patients.

Achieving adequate enteral nutrition among mechanically ventilated patients is challenging, yet critical. We developed NutriSighT, a transformer model using learnable positional coding to predict which patients would achieve hypocaloric nutrition between days 3-7 of mechanical ventilation. Using retrospective data from two large ICU databases (3,284 patients from AmsterdamUMCdb - development set, and 6,456 from MIMIC-IV - external validation set), we included adult patients intubated for at least 72 hours.

Physiological Data Collected from Wearable Devices Identify and Predict Inflammatory Bowel Disease Flares.

Background And Aims: Wearable devices capture physiological signals non-invasively and passively. Many of these parameters have been linked to inflammatory bowel disease (IBD) activity. We evaluated the associative ability of several physiological metrics with IBD flares and how they change before the development of flare.

Detection of neurologic changes in critically ill infants using deep learning on video data: a retrospective single center cohort study.

Background: Infant alertness and neurologic changes can reflect life-threatening pathology but are assessed by physical exam, which can be intermittent and subjective. Reliable, continuous methods are needed. We hypothesized that our computer vision method to track movement, pose artificial intelligence (AI), could predict neurologic changes in the neonatal intensive care unit (NICU).