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LaborEvidence-backed problem
Women Are 26% Of AI Hires. That's A Design Flaw, Not A Shortage
New LinkedIn data shows women are just 26% of AI hires. The gender gap in AI is a design flaw, and almost no one's compensation depends on fixing it.
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1338 published stories · page 33 of 90
Evidence-backed gain
Adaptation of a fair individualized polysocial risk score for hospitalization risk prediction
Objectives We adapted the individualized polysocial risk score (iPsRS), a machine learning model originally developed for patients with type 2 diabetes, to evaluate its generalizability in predicting 1-year hospitalization risk in a disease-agnostic adult cohort, with attention to fairness and explainability. Materials and methods The study utilized de-identified electronic health record data from a retrospective cohort of 17 857 adult patients at the University of Florida Health. The original iPsRS framework wa…
Both readings
Diagnosing melioidosis and tracking treatment outcomes using breath
Melioidosis is a life-threatening infectious disease caused by Burkholderia pseudomallei ( Bp ). Rapid diagnosis and appropriate antimicrobial treatment are critical to reduce mortality, yet diagnosis is hindered by diverse clinical manifestations, mimicry with other diseases, and reliance on slow culture-based methods. Detecting volatile compounds offers a non-invasive approach for rapid infection detection. In this study, we aim to identify volatile compounds in patients' breath that can aid in diagnosing meli…
Evidence-backed gain
Dynamic F1-score-based voting strategies for multi-class classification: an adaptive ensemble approach for non-linear and imbalanced datasets
Classification is a core machine learning task, and ensemble voting methods are widely used to improve predictive accuracy in domains such as medical diagnosis, where class imbalance and non-linear decision boundaries are common. Conventional strategies: Majority Voting (MV), Weighted Voting (WV), and Soft Voting (SV) rely on static or classifier-level weighting schemes that fail to capture per-class differences in classifier reliability. Three dynamic, class-specific voting strategies are introduced: Highest Cl…
Evidence-backed gain
Developing and evaluating automated deep learning and human-in-the-loop vision-language systems for microplastic characterization
Microplastic (MP) pollution poses escalating environmental risks, demanding efficient and reproducible tools for morphological characterization of plastic particles. Traditional manual microscopy is labour-intensive, operator-dependent, and poorly suited to large-scale monitoring. This study presents a comparative evaluation of two distinct artificial intelligence paradigms for the analysis of optical microscope images of microplastics. The first paradigm is a domain-specific, multi-task deep learning (DL) class…
Health
AI-powered medicinal chemistry and translational drug development
Medicinal chemistry sits at the center of modern drug discovery, yet translating molecular designs into approved medicines remains slow, expensive, and prone to high attrition across the pipeline from target identification to clinical validation. Artificial intelligence (AI) is beginning to reshape this landscape by enabling large-scale integration, interpretation, and generation of chemical, biological, and clinical data for hypothesis generation, chemical space exploration, and iterative cycles of model-guided…
Health
The Importance of Artificial Intelligence in Nursing: A Fundamentals of Care Perspective
Aim To analyze the integration of Artificial Intelligence in nursing through the lens of the Fundamentals of Care framework. Design A discursive paper. Methods This discursive paper synthesizes current literature and theoretical perspectives to examine the Relationship, Integration and Context dimensions of the Fundamentals of Care framework in the era of Artificial Intelligence. Results Artificial Intelligence offers substantial benefits in optimizing workflow (Context) and clinical precision (Integration) thro…
Health
Assessing the Utility of Social Determinants of Health Data in Suicide Prediction Models
Objective This study seeks to explore the utility of social determinants of health (SDoH) variables in suicide prediction models. We aim to assess the impact of individual- and geographic-level SDoH factors on improving the performance of suicide prediction models and the identification of individuals at high risk for suicide. Methods A retrospective sample of 1214 deaths by suicide and 815,544 living patients was identified in the Maryland Suicide Data Warehouse (MSDW) and linked to census tract data through ge…
Health
Tissue-Agnostic Cellular Morphometric Biomarkers for Risk-Adapted Management Across Gastrointestinal Precancerous Lesions and Cancers
While precision oncology increasingly adopts tissue-agnostic paradigms, current strategies remain heavily reliant on molecular alterations, with limited relevance to early-stage cancers and precancerous lesion management. Here we present an unsupervised and interpretable artificial intelligence framework that defines tissue-agnostic cellular morphometric biomarkers (CMBs) capturing conserved tumor microenvironment (TME) architectures associated with cancer progression across gastrointestinal (GI) organs. Discove…
Crime
Forensic analysis of dominant versus non-dominant handwriting: Statistical and machine learning insights
Systematic, paired quantitative evidence on how handwriting changes when the non-dominant hand is used remains limited in forensic document examination, despite its frequent relevance in cases involving disguise and authorship concealment. This study presents the first large-scale within-writer analysis examining general and individual handwriting characteristics using statistical testing and predictive modeling. Handwriting samples were collected from 94 right-handed participants, each of whom produced the same…
Media & Arts
Series Brings Vibe Gaming to Life with 'RUN,' An AI
Series Entertainment’s “RUN,” a new platform that lets users create new games while shortening the window for building them down to days.
Media & Arts
Australia bans largely AI-generated songs from official music charts
Australia has banned songs that are largely or wholly generated by AI from its official music charts, requiring eligible releases to be ‘substantially human made’.
