CXR-LLaVA: a multimodal large language model for interpreting chest X-ray images.

Objective: This study aimed to develop an open-source multimodal large language model (CXR-LLaVA) for interpreting chest X-ray images (CXRs), leveraging recent advances in large language models (LLMs) to potentially replicate the image interpretation skills of human radiologists.

Materials And Methods: For training, we collected 592,580 publicly available CXRs, of which 374,881 had labels for certain radiographic abnormalities (Dataset 1) and 217,699 provided free-text radiology reports (Dataset 2). After pre-training a vision transformer with Dataset 1, we integrated it with an LLM influenced by the LLaVA network.

[Study on relations of variables: attributions of somatic symptoms, fatigue, chronic pain and depression in the elderly].

Purpose: The purpose of this study was to investigate the relationships among variables of somatic attribution, chronic pain, depression and chronic fatigue in the elderly.

Methods: Empirical data for testing hypothetical models was collected from 311 people over 65 years old in a community settings in Seoul, Korea in June and July, 2000. Data were analyzed by descriptive statistics and correlational analysis using pc-SAS program.