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| http://dx.doi.org/10.3389/fnetp.2022.996784 | 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.
Artificial Intelligence-Driven Approaches to Managing Surgeon Fatigue and Improving Performance.
Cureus
December 2024
Surgery, Norfolk and Norwich University Hospital, Norwich, GBR.
Surgeon fatigue significantly affects cognitive and motor functions, increasing the risk of errors and adverse patient outcomes. Traditional fatigue management methods, such as structured breaks and duty-hour limits, are insufficient for real-time fatigue detection in high-stakes surgeries. With advancements in artificial intelligence (AI), there is growing potential for AI-driven technologies to address this issue through continuous monitoring and adaptive interventions.
View Article and Find Full Text PDFPrivacy-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 PDFIncreasing exposure to global climate change and hopes for the era of climate adaptation: An aquatic perspective.
Ambio
January 2025
Department of Biological Sciences, Virginia Tech, 926 West Campus Drive, Blacksburg, VA, 24061, USA.
Considerations for adapting digital competencies and training approaches to the public health workforce: an interpretive description of practitioners' perspectives in Canada.
BMC Public Health
January 2025
School of Population and Public Health (SPPH), University of British Columbia (UBC), 2206 East Mall, Vancouver, BC, V6T 1Z3, Canada.
Background: Widespread digital transformation necessitates developing digital competencies for public health practice. Given work in 2024 to update Canada's public health core competencies, there are opportunities to consider digital competencies. In our previous research, we identified digital competency and training recommendations within the literature.
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