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User-Oriented Requirements for Artificial Intelligence-Based Clinical Decision Support Systems in Sepsis: Protocol for a Multimethod Research Project.
JMIR Res Protoc
January 2025
Institute for Health Care Management and Research, University of Duisburg-Essen, Essen, Germany.
Background: Artificial intelligence (AI)-based clinical decision support systems (CDSS) have been developed for several diseases. However, despite the potential to improve the quality of care and thereby positively impact patient-relevant outcomes, the majority of AI-based CDSS have not been adopted in standard care. Possible reasons for this include barriers in the implementation and a nonuser-oriented development approach, resulting in reduced user acceptance.
View Article and Find Full Text PDFEvolution of Artificial Intelligence in Medical Education From 2000 to 2024: Bibliometric Analysis.
Interact J Med Res
January 2025
Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Background: Incorporating artificial intelligence (AI) into medical education has gained significant attention for its potential to enhance teaching and learning outcomes. However, it lacks a comprehensive study depicting the academic performance and status of AI in the medical education domain.
Objective: This study aims to analyze the social patterns, productive contributors, knowledge structure, and clusters since the 21st century.
Risk Factors for Gastrointestinal Bleeding in Patients With Acute Myocardial Infarction: Multicenter Retrospective Cohort Study.
J Med Internet Res
January 2025
Department of Gastroenterology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China.
Background: Gastrointestinal bleeding (GIB) is a severe and potentially life-threatening complication in patients with acute myocardial infarction (AMI), significantly affecting prognosis during hospitalization. Early identification of high-risk patients is essential to reduce complications, improve outcomes, and guide clinical decision-making.
Objective: This study aimed to develop and validate a machine learning (ML)-based model for predicting in-hospital GIB in patients with AMI, identify key risk factors, and evaluate the clinical applicability of the model for risk stratification and decision support.
Predicting the risk of bone metastases in patients with newly diagnosed prostate cancer - Development and validation of a nomogram model.
PLoS One
January 2025
Institute of Translational Medicine, Medical College, Yangzhou University, Yangzhou, Jiangsu, PR. China.
Objectives: The aim of this study was to develop and validate a nomogram model that predicts the risk of bone metastasis (BM) in a prostate cancer (PCa) population.
Methods: We retrospectively collected and analyzed the clinical data of patients with pathologic diagnosis of PCa from January 1, 2013 to December 31, 2022 in two hospitals in Yangzhou, China. Patients from the Affiliated Hospital of Yangzhou University were divided into a training set and patients from the Affiliated Clinical College of Traditional Chinese Medicine of Yangzhou University were divided into a validation set.
Research on technological innovation decision-making considering government subsidies and corporate reputation.
PLoS One
January 2025
School of Business Administration, Shenyang Pharmaceutical University, Shenyang, Liaoning, China.
Aiming at the information asymmetry between pharmaceutical enterprises' technological innovation decisions and government subsidy strategy, this paper establishes a differential game model consisting of the government and a single pharmaceutical company, proposes three different government subsidy strategies, and obtains an equilibrium solution with the help of the Hamilton-Jacobi-Bellman equation, taking into consideration of the transmission effect of the enterprise's reputation. First, the innovation decisions of pharmaceutical firms without government subsidies are analysed, and based on this, the optimal strategies with government subsidies for non-cooperative pacts and cooperation between the government and enterprises are analysed separately. In addition, the effects of different subsidy strategies on the government's investment efficiency, corporate reputation, and the choice of corporate innovation strategies are compared, and the results are verified by numerical analysis.
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