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Reinforced Collaborative-Competitive Representation for Biomedical Image Recognition.
Interdiscip Sci
January 2025
Institute for Complexity Science, Henan University of Technology, Zhengzhou, 450001, China.
Artificial intelligence technology has demonstrated remarkable diagnostic efficacy in modern biomedical image analysis. However, the practical application of artificial intelligence is significantly limited by the presence of similar pathologies among different diseases and the diversity of pathologies within the same disease. To address this issue, this paper proposes a reinforced collaborative-competitive representation classification (RCCRC) method.
View Article and Find Full Text PDFCTCNet: a fine-grained classification network for fluorescence images of circulating tumor cells.
Med Biol Eng Comput
January 2025
Anhui BioX-Vision Biological Technology Co., Ltd, Hefei, 230031, Anhui, China.
The identification and categorization of circulating tumor cells (CTCs) in peripheral blood are imperative for advancing cancer diagnostics and prognostics. The intricacy of various CTCs subtypes, coupled with the difficulty in developing exhaustive datasets, has impeded progress in this specialized domain. To date, no methods have been dedicated exclusively to overcoming the classification challenges of CTCs.
View Article and Find Full Text PDFEvaluating Process and Outcomes of Public Involvement in Applied Health and Social Care Research: A Rapid Systematic Review.
Health Expect
February 2025
Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, UK.
Objective: Public Involvement (PI) in applied health and social care research has grown exponentially in the UK. This review aims to synthesise published UK evidence that evaluates the process and/or outcome(s) of PI in applied health and social care research to identify key contextual factors, effective strategies, outcomes and public partner experiences underpinning meaningful PI in research.
Methods: Following a pre-registered protocol, we systematically searched four databases and two key journals for studies conducted within the UK between January 2006 and July 2024.
Global research trends in the application of artificial intelligence in oncology care: a bibliometric study.
Front Oncol
January 2025
Department of Nursing, Jinjiang Municipal Hospital, Quanzhou, China.
Objective: To use bibliometric methods to analyze the prospects and development trends of artificial intelligence(AI) in oncology nursing from 1994 to 2024, providing guidance and reference for oncology nursing professionals and researchers.
Methods: The core set of the Web of Science database was searched for articles from 1994 to 2024. The R package "Bibliometrix" was used to analyze the main bibliometric features, creating a three-domain chart to display relationships among institutions, countries, and keywords.
Application of artificial intelligence in the health management of chronic disease: bibliometric analysis.
Front Med (Lausanne)
January 2025
School of Nursing, Fujian University of Traditional Chinese Medicine, Fuzhou, China.
Background: With the rising global burden of chronic diseases, traditional health management models are encountering significant challenges. The integration of artificial intelligence (AI) into chronic disease management has enhanced patient care efficiency, optimized treatment strategies, and reduced healthcare costs, providing innovative solutions in this field. However, current research remains fragmented and lacks systematic, comprehensive analysis.
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