Visual-language artificial intelligence system for knee radiograph diagnosis and interpretation: a collaborative system with humans.

ABSTRACT:

Background

Large language models (LLMs) have shown promising abilities in text-based clinical tasks but they do not inherently interpret medical images such as knee radiographs.

Purpose

To develop a human-artificial intelligence interactive diagnostic approach, named radiology generative pretrained transformer (RadGPT), aimed at assisting and synergizing with human users for the interpretation of knee radiological images.

Materials and methods

A total of 22 512 knee roentgen ray images and reports were retrieved from Massachusetts General Hospital; 80% of these were used for model training and 10% were used for model testing and validation, respectively. Fifteen diagnostic imaging features (eg, osteoarthritis, effusion, joint space narrowing, osteophyte) were selected

SUBMITTER: He X 

PROVIDER: S-EPMC12483153 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature