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| http://dx.doi.org/10.3389/fpsyg.2018.01807 | DOI Listing |
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CXR-LLaVA: a multimodal large language model for interpreting chest X-ray images.
Eur Radiol
January 2025
Department of Radiology, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
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.
Bibliometric analysis of laryngeal cancer treatment literature (2003-2023).
Heliyon
January 2025
Department of Otolaryngology Head and Neck Surgery, the Second People's Hospital of Shenzhen, the First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong Province, 518035, China.
Background: Despite advancements in medical science, the 5-year survival rate for laryngeal squamous cell carcinoma remains low, posing significant challenges in clinical management. This study explores the evolution of key topics and trends in laryngeal cancer research. Bibliometric and knowledge graph analysis are utilized to assess contributions in treating this carcinoma and to forecast emerging research hotspots that may enhance future clinical outcomes.
View Article and Find Full Text PDFEffective Structured Information Extraction from Chest Radiography Reports Using Open-Weights Large Language Models.
Radiology
January 2025
From the Departments of Radiology (J.C.G.) and Bioengineering (M.S.Y.), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104.
Privacy-ensuring Open-weights Large Language Models Are Competitive with Closed-weights GPT-4o in Extracting Chest Radiography Findings from Free-Text Reports.
Radiology
January 2025
From the Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany.
Background Large-scale secondary use of clinical databases requires automated tools for retrospective extraction of structured content from free-text radiology reports. Purpose To share data and insights on the application of privacy-preserving open-weights large language models (LLMs) for reporting content extraction with comparison to standard rule-based systems and the closed-weights LLMs from OpenAI. Materials and Methods In this retrospective exploratory study conducted between May 2024 and September 2024, zero-shot prompting of 17 open-weights LLMs was preformed.
View Article and Find Full Text PDFExperiences of African women who migrate to a developed country and encounter intimate partner violence: a systematic review of qualitative evidence.
JBI Evid Synth
January 2025
School of Nursing and Midwifery, University of Newcastle, Newcastle, New South Wales, Australia.
Objective: The objective of this review was to synthesize the available evidence on the experiences of African women who migrated to a developed country and encountered intimate partner violence (IPV).
Introduction: IPV is a significant public health issue, and migrant women living in developed countries are particularly vulnerable to IPV, experiencing disproportionately higher rates of IPV. Understanding the experiences of these women can inform health policy and decision-making in clinical practice to minimize IPV.
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