+ HEALTH Background Discharge letters are essential for continuity of care, yet patients often misunderstand their con…± HEALTH Hematoxylin and Eosin (H&E) glass slide preparation is fundamental to histopathology. Pathologists' expertise…+ CLIMATE Traditional soil heavy metal risk assessments suffer from fragmented indices and poor logical integration. Co…± HEALTH Background Neonatal pneumothorax can progress rapidly, yet detection on supine chest radiographs remains chal…+ HEALTH T cell exhaustion has widespread implications for the progression and treatment of chronic diseases including…+ HEALTH Background Standard pulmonary arterial hypertension (PAH) risk models may not fully apply to PAH associated w…+ HEALTH Objectives To map and synthesize the scientific evidence regarding the use of Augmented Reality (AR) and Mixe…± HEALTH Objective To examine whether the outputs of machine learning algorithms designed to predict risk of cardiovas…
TruaceTracing the truth around AIMonday, September 14, 2026
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1045 published stories · page 10 of 70

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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…

Xenotransplantation · Health

Artificial Intelligence in Xenotransplantation: A Prioritized Roadmap for Early Clinical Translation, Opportunities and Challenges
Evaluating the Accuracy, Empathy, and Readability of Generative AI Versus Registered Nurses in Discharge Planning: A Vignette-Based Study
Evidence-backed problem

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…

Health
Precision Livestock Farming Technologies for Sheep Welfare in Extensive Systems: A Comprehensive Review
Evidence-backed gain

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…

Other
Forest Kernel Balancing Weights: Outcome-Guided Features for Causal Inference
Evidence-backed gain

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…

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

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
Both readings

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
Evidence-backed gain

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…

Health

Sony and Warner sue Anthropic over copyrighted song theft used for AI training

Sony Music Publishing and Warner Chappell Music have sued Anthropic, accusing the $2 trillion AI company of pirating copyrighted songs at massive scale to train Claude, its family of AI models. The lawsuit, filed Friday in California federal court, alleges Anthropic obtained lyrics and sheet music through torrent downloads and other pirate sources rather than

Media & Arts
Sony and Warner sue Anthropic over copyrighted song theft used for AI training

Advancing hirschsprung disease diagnosis: a systematic review of the development and application of artificial intelligence in histopathological analysis

Hirschsprung's disease (HD) is characterised by absence of ganglion cells in the distal large intestine, requiring accurate histopathological diagnosis. Conventional diagnostic methods are time-consuming, subjective, and demand specialised expertise. While artificial intelligence (AI) shows promise for improving diagnostic capacity, its clinical utility requires rigorous evaluation. Following PRISMA 2020 guidelines, this systematic review evaluated machine and deep learning techniques for HD diagnosis from histo…

Health
Advancing hirschsprung disease diagnosis: a systematic review of the development and application of artificial intelligence in histopathological analysis

Technology and Obesity: A Year in Review

SMART technological advancements help diagnose, treat, and monitor various diseases at the earliest stages. It presents an opportunity to maintain the key components of conventional obesity management programming while reducing costs and provider time inputs. Various machine learning models have helped predict the risks of obesity and metabolic syndrome. Additionally trained convolutional neural networks can now automatically segment and quantify different adipose tissue compartments. Various multicenter series…

Health
Technology and Obesity: A Year in Review

Harnessing machine learning, docking and molecular dynamics for the virtual screening of compounds as CDK4/6 dual inhibitors

Cyclin-dependent kinases 4 and 6 (CDK4/6) are pivotal regulators of the G1-to-S phase transition, and their dysregulation is a hallmark of numerous malignancies. Despite the clinical success of existing CDK4/6 inhibitors, there remains a persistent need for chemically diverse scaffolds with potent dual-target affinity. In this study, we developed and implemented a virtual screening workflow that synergistically integrates ligand-based machine learning with structure-based molecular docking. By benchmarking multi…

Health
Harnessing machine learning, docking and molecular dynamics for the virtual screening of compounds as CDK4/6 dual inhibitors

Mapping agricultural fragility in India through a yield gap vulnerability framework: a national-scale machine learning assessment

In India, regional yield gaps continue to widen despite increased agricultural productivity, owing to socioeconomic inequality and climate variability. This study develops a novel Yield Gap Vulnerability (YGV) framework to measure agricultural fragility at the district-level by integrating agricultural, hydrological, meteorological, and socioeconomic indicators to observed yield gaps for major cereals (rice and wheat) and nutri-crops (maize and millet). An integrated Machine Learning (ML) approach is used that c…

Crime
Mapping agricultural fragility in India through a yield gap vulnerability framework: a national-scale machine learning assessment