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1382 published stories · page 57 of 93

Global genomic surveillance of β-lactam resistance in Escherichia coli across human, animal, and environmental reservoirs
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Global genomic surveillance of β-lactam resistance in Escherichia coli across human, animal, and environmental reservoirs

Background Escherichia coli poses a global health threat from increasing β-lactam resistance. This study uses genomic and One-Health data to map resistance patterns and enhance antimicrobial resistance (AMR) prediction and management. Methods This study performed a One-Health whole-genome analysis of 30,554 E. coli isolates from human, animal, and environmental sources, spanning from 2000 to 2025. Publicly available genomic data were retrieved from NCBI, encompassing β-lactam resistance genes, including extended…

Infection, Genetics and Evolution
Artificial intelligence and machine learning in pharmaceutical research and healthcare: Ethical challenges and a framework for responsible implementation
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Artificial intelligence and machine learning in pharmaceutical research and healthcare: Ethical challenges and a framework for responsible implementation

Background Artificial intelligence (AI) and machine learning (ML) are transforming pharmaceutical research and healthcare by enabling analysis of large-scale biomedical data and supporting data-driven decision-making. However, their rapid integration has introduced significant ethical, governance, and implementation challenges that remain insufficiently synthesized within a unified framework. Objective This work aims to synthesize the central ethical challenges and paradoxes associated with AI and ML in pharmace…

Research and application of machine learning models based on multimodal big data for precise transfusion management in acute myeloid leukaemia
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Research and application of machine learning models based on multimodal big data for precise transfusion management in acute myeloid leukaemia

Acute myeloid leukaemia (AML) is a highly heterogeneous haematologic malignancy in which transfusion support represents an essential component of comprehensive patient care. This review aims to provide an updated synthesis of recent progress in the development and clinical application of machine learning models based on multimodal big data for precision transfusion management in AML, addressing the persistent limitations of conventional, empirically guided transfusion practices. We systematically reviewed the li…

Health

Beyond Thresholds: Can Machine Learning Improve Trauma Field Triage?

BackgroundAccurate triage of trauma patients by Emergency Medical Services (EMS) is essential for optimal outcomes and resource allocation. The 2021 National Field Triage Guidelines (FTG) assist EMS in prehospital triage; however, its collective performance has never been evaluated using a national database. We aimed to evaluate an FTG surrogate and develop a predictive model to identify patients at risk for serious injury.MethodsThe Trauma Quality Improvement Program National Trauma Databank (2017-2020) was que…

Beyond Thresholds: Can Machine Learning Improve Trauma Field Triage?
Health

AI-based clinician decision support system for diagnosis of inherited retinal diseases: a multicenter, randomized trial

The accurate and timely diagnosis of inherited retinal diseases (IRDs) represents an unmet clinical need in ophthalmology, as the current pathways rely on resource-intensive phenotyping, multidisciplinary expertise and genetic testing. Here we developed Retina4IRD, an artificial intelligence (AI)-based clinician decision support system (CDSS) that predicts 17 genotype categories from retina images. Retina4IRD uses a Vision Transformer model pretrained with RETFound. We then trained and validated Retina4IRD using…

AI-based clinician decision support system for diagnosis of inherited retinal diseases: a multicenter, randomized trial
Health

Machine Learning Prediction of Hoehn and Yahr Scores at 5-Years Post-<sup>123</sup>I-Ioflupane SPECT Imaging in a Real-World Parkinson's Disease Dataset

Background Parkinson's disease (PD) progression is highly heterogeneous, complicating clinical management and prognostication. While machine learning models have been developed using research datasets such as Parkinson's Precision Medicine Initiative (PPMI) and Parkinson's Disease Biomarkers Program (PDBP), their clinical translatability is limited due to differences in routinely collected data. The Hoehn and Yahr (H&Y) scale is commonly used in clinical practice to stage PD, yet most predictive models focus on…

Machine Learning Prediction of Hoehn and Yahr Scores at 5-Years Post-<sup>123</sup>I-Ioflupane SPECT Imaging in a Real-World Parkinson's Disease Dataset
Education

Embracing the future of Artificial Intelligence in the classroom: the relevance of AI literacy, prompt engineering, and critical thinking in modern education

Abstract The present discussion examines the transformative impact of Artificial Intelligence (AI) in educational settings, focusing on the necessity for AI literacy, prompt engineering proficiency, and enhanced critical thinking skills. The introduction of AI into education marks a significant departure from conventional teaching methods, offering personalized learning and support for diverse educational requirements, including students with special needs. However, this integration presents challenges, includin…

Embracing the future of Artificial Intelligence in the classroom: the relevance of AI literacy, prompt engineering, and critical thinking in modern education
Health

Early Identification of Recovery Potential After Acute Brain Injury Using Functional Near-Infrared Spectroscopy

Background Accurate early prognostication in patients with acute brain injury remains a major challenge in neurocritical care. Conventional bedside assessments provide limited insight into long-term outcomes and may not fully capture preserved brain function that supports recovery. Functional neuroimaging can detect brain activity not evident at the bedside, but its use in intensive care remains constrained by cost, logistics, and the need for stronger evidence supporting its value. Functional near-infrared spec…

Early Identification of Recovery Potential After Acute Brain Injury Using Functional Near-Infrared Spectroscopy
Health

Treatment-Effect-Based Versus Risk-Based Targeting of Care Management Outreach in Medicaid: A Retrospective Cohort Study with Machine Learning

Medicaid care-management programs typically allocate scarce outreach capacity to beneficiaries with the highest predicted risk of an acute event, assuming that risk and responsiveness are aligned and stable across short intervals. The authors tested whether targeting outreach by predicted individualized treatment effect-the conditional average treatment effect (CATE) recomputed each calendar month-outperforms risk-based targeting. The authors analyzed 164,063 adult Medicaid beneficiaries (2,670,806 person-months…

Treatment-Effect-Based Versus Risk-Based Targeting of Care Management Outreach in Medicaid: A Retrospective Cohort Study with Machine Learning
Health

Multispecialty Dental EMRs from Chairside Audio: An Exploratory Study

The objective of this study was to develop and internally evaluate a modular large language model (LLM) system for generating standardized electronic medical records (EMRs) from dental chairside consultations under conditions of acoustic interference and specialty-specific heterogeneity. We built a controllable pipeline integrating multistage audio enhancement and local automatic speech recognition with a cascaded LLM generator. A baseline end-to-end system (system 1) was compared with an evidence-enhanced syste…

Multispecialty Dental EMRs from Chairside Audio: An Exploratory Study