± HEALTH Large language models (LLMs) are artificial intelligence (AI) tools specifically trained to process and gener…+ 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…
TruaceTracing the truth around AIMonday, September 14, 2026
Subscribe

All stories

1046 published stories · page 22 of 70

Both readings

The electrocardiogram and artificial intelligence: turning signals into insights in pediatric and congenital heart disease

Purpose of review Artificial intelligence applied to electrocardiography (AI-ECG) has rapidly been investigated in adult cardiovascular medicine, yet translation into pediatric and congenital heart disease populations has lagged. This review summarizes contemporary AI-ECG methodologies and emerging applications in pediatric and congenital heart disease (PCHD), with emphasis on current clinical utility, technical challenges, and future opportunities for implementation. Recent findings Recent studies demonstrate t…

Current Opinion in Pediatrics · Health

The electrocardiogram and artificial intelligence: turning signals into insights in pediatric and congenital heart disease
Development and External Validation of an AI-ECG Algorithm for Estimating Elevated Serum NT-proBNP Levels
Evidence-backed gain

Development and External Validation of an AI-ECG Algorithm for Estimating Elevated Serum NT-proBNP Levels

Background N-terminal pro-B-type natriuretic peptide (NT-proBNP) is a cornerstone biomarker for the diagnosis and management of heart failure, but its use may be limited by the need for blood testing and laboratory infrastructure. Artificial intelligence (AI) applied to electrocardiograms (ECGs) may offer a widely accessible, non-invasive approach to estimate NT-proBNP levels. Methods We developed a convolutional neural network incorporating residual and attention-based layers to estimate NT-proBNP levels from s…

Health
Regulatory Research Priorities for AI Use in the Medicine Lifecycle: A European Perspective with Global Relevance
Both readings

Regulatory Research Priorities for AI Use in the Medicine Lifecycle: A European Perspective with Global Relevance

Regulatory bodies play a central role in providing guidance that enables safe and effective use of artificial intelligence tools in medicine development and evaluation. Regulators can also act as catalysts for regulatory science research. To inform these efforts, a European-wide survey was conducted to solicit stakeholder perspectives on the priority areas for regulatory science research related to the use of artificial intelligence in the medicine lifecycle. Twenty-eight regulatory science research questions we…

Policy
Reframing risk management for AI-enabled medical devices: A dual-layer risk governance framework
Both readings

Reframing risk management for AI-enabled medical devices: A dual-layer risk governance framework

BackgroundAI-enabled medical devices introduce dynamic, data-dependent risks that challenge traditional safety-risk management frameworks. While ISO 14971, AAMI CR34971, and the EU Artificial Intelligence Act each address elements of device safety and algorithmic governance, they remain fragmented when applied. This review examines conceptual and operational gaps in current approaches and proposes an integrated governance model for AI-specific safety-risk management.MethodsA structured narrative review was condu…

Policy
Δ -Machine Learning for the Prediction of Metal Complex Properties
Evidence-backed gain

Δ -Machine Learning for the Prediction of Metal Complex Properties

The discovery and design of novel transition metal complexes for specific applications heavily rely on computational high-throughput screenings to identify promising candidates for experimental validation. However, traditional computational approaches, such as density functional theory, are often too computationally demanding to be applied on a large scale. Machine learning methods offer a promising alternative due to their excellent computational efficiency, but their accuracy and high data requirements remain…

Science
Deep Learning-Based Enhancement of Already Diagnostic-Quality MRI for Alzheimer's Disease Classification: Effects on Model Performance and Training Data Requirements
Evidence-backed gain

Deep Learning-Based Enhancement of Already Diagnostic-Quality MRI for Alzheimer's Disease Classification: Effects on Model Performance and Training Data Requirements

Background Deep learning (DL)-based image enhancement is widely used to improve suboptimal medical imaging. Whether it also benefits diagnostic-quality MRI in downstream task performance and data-efficiency remains unclear. Purpose To investigate the impact of DL-based enhancement applied to diagnostic quality structural MRI for Alzheimer's disease (AD) classification. Study type Retrospective. Population A total of 2293 brain MRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were split into…

Health
Machine-learning-based Phenomapping of Patients with Keratinocyte Carcinoma: Data-driven Subgrouping by Disease Burden, Comorbidities and Socioeconomic Status
Evidence-backed gain

Machine-learning-based Phenomapping of Patients with Keratinocyte Carcinoma: Data-driven Subgrouping by Disease Burden, Comorbidities and Socioeconomic Status

Keratinocyte carcinoma (KC) places a considerable and growing burden on healthcare systems. Given the KC population's heterogeneity, tailored clinical pathways are needed to accommodate diverse management needs. This study applied machine learning (ML)-based phenomapping to identify distinct real-world subgroups within a national KC population using demographic and medical history variables. The study included KC patients treated in publicly-funded, office-based dermatology practices and registered in the Danish…

Health

Efficient video-based traffic conflict prediction and interpretable risk analysis at signalized intersections via deep learning

Objectives To achieve accurate and real-time prediction of traffic conflicts at signalized intersections and identify their key contributing factors, thereby supporting proactive safety management and reducing accident risks. Methods This study proposes a novel multi-stage traffic-conflict prediction framework that integrates a real-time video image processing system and an advanced conflict-prediction model. Specifically, a real-time video analysis system integrating the YOLOv8 object detection framework and th…

Policy
Efficient video-based traffic conflict prediction and interpretable risk analysis at signalized intersections via deep learning

AI Simplification of Dermatopathology Reports for Patients: Basic Versus Prompt-Engineered Approaches

Background Patients struggle to comprehend dermatopathology reports. As artificial intelligence (AI) tools become more accessible, patients may use them to interpret reports; however, optimal approaches remain unexplored. Objective Evaluate whether prompt-engineered AI simplification of dermatopathology reports improves factualness, completeness, and reduces potential harm compared to basic AI usage. Methods Survey-based study (January-April 2025) of 52 US dermatology and dermatopathology professionals (70.3% re…

Health
AI Simplification of Dermatopathology Reports for Patients: Basic Versus Prompt-Engineered Approaches

PREDICTIVE OCT BIOMARKERS OF RETINAL CHANGES AND VISUAL OUTCOMES IN SILICONE OIL ENDOTAMPONADE IDENTIFIED BY ARTIFICIAL INTELLIGENCE

Purpose To quantify retinal layer changes and visual outcomes in eyes with silicone oil (SO) endotamponade for rhegmatogenous retinal detachment using OCT biomarkers and prediction models. Methods Seventy-six eyes with SO endotamponade underwent macular volume OCT at SO insertion and just before SO removal. An automated segmentation tool quantified retinal nerve fiber layer (RNFL), ganglion cell layer + inner plexiform layer (GCL+IPL), other retinal layers, and fluid (SRF, IRF). Eyes with and without macular ede…

Health
PREDICTIVE OCT BIOMARKERS OF RETINAL CHANGES AND VISUAL OUTCOMES IN SILICONE OIL ENDOTAMPONADE IDENTIFIED BY ARTIFICIAL INTELLIGENCE

Impact of AI assistance on reading time, cancer detection rate, and abnormal interpretation rate in screening and diagnostic mammography: a prospective alternating-month study

Objective To compare reading time, cancer detection rate (CDR), and abnormal interpretation rate (AIR) between AI-assisted and non-AI-assisted periods in screening and diagnostic mammography performed in routine clinical practice. Materials and methods We prospectively collected reading times for consecutive two-view full-field digital mammography interpreted by four radiologists between August 2023 and July 2024. Both screening and diagnostic examinations were included. A commercially available AI system was in…

Health
Impact of AI assistance on reading time, cancer detection rate, and abnormal interpretation rate in screening and diagnostic mammography: a prospective alternating-month study

As online dating goes into ‘salvage mode’, can AI solve all its problems?

Bumble was supposed to be different. When it launched in 2014, it offered women a simple proposition: if you matched with a man, you had to make the first move. The rule was meant to make online dating safer and less intimidating, while giving women control over who got to speak first. This week, Bumble abandoned it. The change follows the introduction of Opening Moves, which had already softened the original rule, and comes as Bumble prepares to abandon another defining feature: the swipe. The company says it i…

Lifestyle
As online dating goes into ‘salvage mode’, can AI solve all its problems?

Generative AI for Transformative Healthcare: A Comprehensive Study of Emerging Models, Applications, Case Studies, and Limitations

Generative artificial intelligence (GAI) can be broadly described as an artificial intelligence system capable of generating images, text, and other media types with human prompts. GAI models like ChatGPT, DALL-E, and Bard have recently caught the attention of industry and academia equally. GAI applications span various industries like art, gaming, fashion, and healthcare. In healthcare, GAI shows promise in medical research, diagnosis, treatment, and patient care and is already making strides in real-world depl…

Health
Generative AI for Transformative Healthcare: A Comprehensive Study of Emerging Models, Applications, Case Studies, and Limitations