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1313 published stories · page 21 of 88

UK economy unexpectedly grows 0.4% in July boosted by AI
BusinessEvidence-backed gain

UK economy unexpectedly grows 0.4% in July boosted by AI

The UK economy grew in July as the rapid expansion of AI appeared to outweigh the economic damage from the Iran war, in a welcome boost for John Healey. Figures from the Office for National Statistics (ONS) showed a surprise 0.4% increase in gross domestic product (GDP), compared with 0.3% growth in June. City economists had forecast zero growth. The figures suggested the economy continued to be robust despite the fallout from the Iran war, which has raised energy costs and led to higher interest rates than were…

The Guardian
FedMediFormer-XAI: Federated Multimodal Transformers with Diffusion Augmentation and Graph-Based Drug Recommendation for Diabetes
Evidence-backed gain

FedMediFormer-XAI: Federated Multimodal Transformers with Diffusion Augmentation and Graph-Based Drug Recommendation for Diabetes

Diabetes management faces a number of obstacles, such as fragmented healthcare data, privacy concerns, poor explainability, and the lack of personalized therapeutic guidance. This research work introduces FedMediFormer-XAI, a unified and proper framework that incorporates federated learning, multimodal transformers, diffusion-based data augmentation, Graph Neural Networks (GNNs) for drug recommendation, and Explainable Artificial Intelligence (XAI) for diabetes intelligence. The system utilizes diverse healthcar…

Artificial intelligence -based modeling and comparative evaluation of craniofacial soft tissue and subcutaneous fat thickness using ct imaging for forensic identification in a northwestern indian population
Evidence-backed problem

Artificial intelligence -based modeling and comparative evaluation of craniofacial soft tissue and subcutaneous fat thickness using ct imaging for forensic identification in a northwestern indian population

Background Craniofacial soft tissue thickness (CFSTT) is a critical parameter in forensic facial reconstruction, serving as a link between skeletal morphology and facial appearance. However, most existing studies rely on mean CFSTT values without considering the contribution of subcutaneous fat layer thickness, limiting the accuracy of reconstruction models. Objective This study aims to develop an artificial intelligence (AI)-assisted, CT-based framework for the comparative evaluation of craniofacial soft tissue…

Advancing preoperative planning technology in total joint arthroplasty with a real-time machine learning calculator: 1-year mortality risk in a value-based care era
Evidence-backed gain

Advancing preoperative planning technology in total joint arthroplasty with a real-time machine learning calculator: 1-year mortality risk in a value-based care era

Background Technological innovation in total joint arthroplasty (TJA) has largely focused on intraoperative precision through robotics, navigation, and implant design, while preoperative decision-making remains comparatively underdeveloped. Accurate estimation of patient-specific risk is central to surgical indications, yet existing tools provide limited resolution for consequential outcomes such as 1-year mortality. Methods A machine learning model was developed using the TriNetX Research Network. Patients unde…

Machine Learning for Mortality Prediction in Infective Endocarditis: A Systematic Review and Meta-Analysis
Both readings

Machine Learning for Mortality Prediction in Infective Endocarditis: A Systematic Review and Meta-Analysis

Infective endocarditis (IE) continues to be an often fatal condition despite improvements in cardiac surgical procedures and antibiotic therapy, and conventional scoring tools show poor generalizability. Machine learning (ML) addresses these limitations by capturing complex, nonlinear clinical relationships, outperforming conventional scores in predictive accuracy, though prior ML work in IE has focused on diagnosis. A PRISMA-compliant systematic review and meta-analysis of PubMed (Supplemental Digital Content,…

Health

Artificial intelligence for lung disease quantification in systemic sclerosis-associated interstitial lung disease and other connective tissue disease-associated interstitial lung disease

Purpose of review Systemic sclerosis-associated interstitial lung disease (SSc-ILD) is the leading cause of disease-related mortality in systemic sclerosis and the connective tissue disease-associated ILD (CTD-ILD) in which artificial intelligence imaging has advanced most rapidly. Visual high-resolution CT (HRCT) scoring is reader-dependent and limits clinical decision-making. This review summarizes clinically relevant artificial intelligence and radiomics publications from approximately the last 18 months, foc…

Artificial intelligence for lung disease quantification in systemic sclerosis-associated interstitial lung disease and other connective tissue disease-associated interstitial lung disease
Health

How Well Do AI Chatbots Understand Abnormal Anatomy: A Comparative Study Using Congenital Anomalies and Tumor Cases

Artificial Intelligence (AI) chatbots are becoming an efficient option to understand medical data and also help with clinical reasoning. There has been a recent progression in research of large language models and their ability to be used in the healthcare sector, such as radiological image analysis, and diagnostic support. There is however, little evidence supporting their ability to accurately understand abnormal anatomical conditions, such as congenital anomalies and tumor related changes in anatomy. To asses…

How Well Do AI Chatbots Understand Abnormal Anatomy: A Comparative Study Using Congenital Anomalies and Tumor Cases
Health

Stakeholder perspectives on artificial intelligence in schizophrenia care

Background Artificial intelligence (AI) is explored as a tool to expand access to mental health care, offering support and self-disclosure. To date, the perspectives of individuals with schizophrenia on AI have been absent from the medical literature. This exploratory study represents an effort to report schizophrenia patient perspectives on AI chatbots through a focus group. Methods We conducted an exploratory, cross-sectional, qualitative study using semi-structured focus groups at a large academic health cent…

Stakeholder perspectives on artificial intelligence in schizophrenia care
Health

Artificial Intelligence and Complementary Digital Health Technologies Across the Travel Medicine Continuum: A Narrative Review

Background Artificial intelligence (AI) and complementary digital health technologies are increasingly being applied to improve prevention, diagnosis and surveillance in travel medicine. This narrative review evaluates current applications of these technologies across the pre-travel, peri-travel and post-travel phases of the travel continuum. Methods A narrative literature review was conducted using a clinically oriented three-phase framework encompassing pre-travel preparation, peri-travel monitoring and post-t…

Artificial Intelligence and Complementary Digital Health Technologies Across the Travel Medicine Continuum: A Narrative Review
Health

Effect of Large Language Model-Powered Virtual Standardized Patients on History-Taking Among Undergraduate Medical Students: Propensity-Matched Cohort Study

Background Medical history taking (MHT) is a foundational clinical competency for medical students; however, traditional training models using standardized patients face challenges such as resource constraints. Large language model-powered virtual standardized patients (LLM-VSPs) offer a safe, repeatable platform for self-directed practice with AI-automated feedback. Nevertheless, their effectiveness in authentic teaching environments and underlying learning mechanisms require further investigation. Objective Th…

Effect of Large Language Model-Powered Virtual Standardized Patients on History-Taking Among Undergraduate Medical Students: Propensity-Matched Cohort Study
Health

Beyond the Algorithm: A Stewardship Framework for the Hand Surgeon Adopting Artificial Intelligence

Artificial intelligence is entering hand surgery through imaging, outcome prediction, and patient communication. Neural networks read scaphoid and distal radius radiographs. Machine learning models predict outcomes after carpal tunnel release. Large language models are being tested for patient communication and chart drafting. Adoption, however, has outpaced validation. Most hand surgery artificial intelligence tools are tested only on data resembling their training set, deployed in workflows that have not been…

Beyond the Algorithm: A Stewardship Framework for the Hand Surgeon Adopting Artificial Intelligence
Health

Pediatric Artificial Intelligence in Radiology: Unmet Needs and Next Steps-<i>AJR</i> Expert Panel Review

Artificial intelligence (AI) applications have transformed radiology, yet pediatric medical imaging remains substantially underrepresented in AI development, validation, regulation, and implementation. Unlike adults, children go through continuous physiologic and anatomic changes that require age-specific models trained on representative developmental data. However, pediatric AI is limited by scarce publicly available datasets, fragmented institutional data, rare diseases, heterogeneous reporting practices, and…

Pediatric Artificial Intelligence in Radiology: Unmet Needs and Next Steps-<i>AJR</i> Expert Panel Review