The potential of Generative Pre-trained Transformer 4 (GPT-4) to analyse medical notes in three different languages: a retrospective model-evaluation study.

Background: Patient notes contain substantial information but are difficult for computers to analyse due to their unstructured format. Large-language models (LLMs), such as Generative Pre-trained Transformer 4 (GPT-4), have changed our ability to process text, but we do not know how effectively they handle medical notes. We aimed to assess the ability of GPT-4 to answer predefined questions after reading medical notes in three different languages.

MRI spine request form enhancement and auto protocoling using a secure institutional large language model.

Background Context: Secure institutional large language models (LLM) could reduce the burden of noninterpretative tasks for radiologists.

Purpose: Assess the utility of a secure institutional LLM for MRI spine request form enhancement and auto-protocoling.

Study Design/setting: Retrospective study conducted from December 2023 to February 2024, including patients with clinical entries accessible on the electronic medical record (EMR).

Applications of Artificial Intelligence and Machine Learning in Spine MRI.

Diagnostic imaging, particularly MRI, plays a key role in the evaluation of many spine pathologies. Recent progress in artificial intelligence and its subset, machine learning, has led to many applications within spine MRI, which we sought to examine in this review. A literature search of the major databases (PubMed, MEDLINE, Web of Science, ClinicalTrials.