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1338 published stories · page 29 of 90

LifestyleEvidence-backed problem

Portable Raman spectroscopy coupled with machine learning for rapid identification and source apportionment of plastic particles in aquaculture wastewater

Aquaculture expansion has exacerbated microplastics (MPs) contamination in aquaculture water bodies. MPs readily adsorb heavy metals and organic pollutants to form composite contamination and accumulate through food chains, imposing ecological and human health risks. Conventional detection techniques including microscopic observation, FTIR and Py-GC-MS are limited by low identification accuracy, matrix interference, destructiveness and cumbersome procedures, which cannot support rapid large-scale monitoring. Ram…

Analytical Methods

Evidence-backed gain

Machine learning-assisted nitrogen-doped carbon dots for Fe<sup>3+</sup> detection in aqueous environments

Evidence-backed gain

Data-efficient exploration of atomic clusters via uncertainty quantification on complex potential energy surfaces

Both readings

Artificial intelligence-enabled causal estimate of Medicare drug plan integration in cancer care: A doubly robust machine learning instrumental variable analysis

Both readings

An informatics framework to harmonize electronic health record medication data for managed care analytics and artificial intelligence applications

Background Artificial intelligence (AI) applications in managed care pharmacy depend on semantically consistent medication data, yet heterogeneous medication identifiers across real-world electronic health records (EHRs) could undermine analytic fidelity and risk propagating classification errors. To enable transportable, reproducible AI tools, methods for harmonizing disparate medication identifiers (eg, National Drug Code [NDC] and Multum drug synonym ID) to standardized vocabularies are required. Objective To…

Both readings

A pharmacist-overseen, artificial intelligence-enabled model for provider-side prior authorization: From burden to opportunity (PAVE-1)

Prior authorization (PA) imposes substantial administrative burdens on clinicians, contributing to burnout, delayed care, and excess health care spending. Artificial intelligence (AI) is emerging as a tool to automate PA tasks in managed care pharmacy, yet concerns persist regarding transparency, bias, and overreliance on autonomous systems. This is particularly true in payer-deployed AI systems that may deny claims without adequate clinical review. This viewpoint proposes a pharmacist-overseen, AI-enabled syste…

Both readings

[Artificial intelligence in cardiovascular prevention: a strategic opportunity for risk prediction and early diagnosis]

Cardiovascular diseases remain the leading cause of mortality and morbidity worldwide, with substantial impact in Italy. Cardiovascular prevention is a strategic priority, yet a significant gap persists between evidence-based guideline recommendations and their actual implementation in clinical practice. Artificial intelligence (AI), through machine learning and deep learning models, is emerging as a potentially transformative technology to bridge this gap, enabling more precise, dynamic, and personalized cardio…

Evidence-backed gain

[Breast arterial calcifications and cardiovascular risk in women]

Cardiovascular (CV) diseases remain the leading cause of mortality in women and often present as the first clinical manifestation, highlighting the limitations of current risk scores. In this context, breast arterial calcifications (BACs), incidentally detected on screening mammography, are emerging as a potential biomarker of systemic CV risk. This narrative review summarizes the most recent evidence (2023-2026) on the association between BACs and CV outcomes, including observational studies, prospective and re…

Health

Artificial Intelligence in Xenotransplantation: A Prioritized Roadmap for Early Clinical Translation, Opportunities and Challenges

Xenotransplantation represents a potential solution to the persistent global organ shortage, yet its clinical application remains stalled by complex immunologic responses, coagulation dysregulation, species-specific biology, and infectious risks. Artificial intelligence (AI) could enhance safety, accelerate decision-making, and enable precision medicine initiatives within this rapidly evolving field. However, effective implementation of AI in xenotransplantation requires approaches specifically adapted to the bi…

Health

Evaluating the Accuracy, Empathy, and Readability of Generative AI Versus Registered Nurses in Discharge Planning: A Vignette-Based Study

Aim To compare the multidimensional performance of discharge instructions generated by generative AI (GPT-4) versus those created by clinical registered nurses across three dimensions-accuracy, empathy and readability-and to explore the impact of patient. Design A prospective, double-blind, vignette-based cross-sectional study. Methods Five standardized multidisciplinary discharge scenarios were constructed. Discharge instructions were generated independently by five registered nurses and GPT-4. Fifteen clinical…

Other

Precision Livestock Farming Technologies for Sheep Welfare in Extensive Systems: A Comprehensive Review

Background Ensuring animal welfare in extensive sheep production systems remains challenging due to large grazing areas, limited human supervision and the difficulty of detecting early signs of health or behavioural problems. Precision Livestock Farming (PLF) technologies have emerged as promising tools to enhance monitoring and welfare assessment in such environments. Objective This review examines recent advances in PLF technologies and their potential contribution to improving sheep welfare in extensive farmi…

Science

Forest Kernel Balancing Weights: Outcome-Guided Features for Causal Inference

While balancing covariates between groups is central for observational causal inference, selecting which features to balance remains a challenging problem. Kernel balancing is a promising approach that first estimates a kernel that captures similarity across units and then balances a (possibly low-dimensional) summary of that kernel, indirectly learning important features to balance. In this paper, we propose forest kernel balancing, which leverages the underappreciated fact that tree-based machine learning mode…

Health

Multisite Implementation of a Digital Wound Model of Care: A Post-Implementation Multimethods Evaluation of Patient and Clinician Perspectives and Lessons Learned

This study evaluated district-wide implementation of a digital wound model of care combining an artificial intelligence-enabled application with a virtual command centre across four hospitals and five community health centres in Australia. A post-implementation multimethods evaluation (January 2024-January 2026) of patients (n = 94), frontline clinicians (n = 75), and senior wound nurses (n = 9) and a product manager (n = 1) using surveys, semi-structured interviews and analysis of governance meeting minutes. Pa…

Health

A machine learning-derived sarcopenia index is associated with survival and nonrelapse mortality in DLBCL

Abstract Body composition analysis (BCA) provides an objective assessment of metabolic states, but its prognostic value in diffuse large B-cell lymphoma (DLBCL) remains unclear. We applied machine learning-supported BCA to computed tomography imaging from patients with newly diagnosed DLBCL enrolled in the prospective phase 3 PETAL trial to quantify radiologic sarcopenia. We assessed BCA results in relation to survival after first-line immunochemotherapy, treatment-related hematologic toxicities, and molecular d…

Health

Explainable Plasma Proteomics-Based Machine Learning for Osteoporosis Diagnosis, Prognosis, and Protein Biomarker Discovery in the UK Biobank

Osteoporosis (OP) is often underdiagnosed, highlighting the need for tools that can both detect existing disease and predict future risk; large-scale plasma proteomics combined with explainable machine learning enables integrated diagnostic and prognostic modeling while prioritizing clinically relevant protein markers. This study aims to develop and validate an explainable plasma proteomics machine-learning framework for osteoporosis diagnosis, future risk prediction, and biomarker discovery. We further tested w…