± HEALTH Colorectal Cancer (CRC) is a common cause of cancer death and prognostic factors are used to determine manage…+ HEALTH This multicenter study developed and validated an interpretable machine learning model integrating granular n…+ HEALTH Multiple stakeholders need to locate results of registered clinical trials but frequently struggle to find th…+ HEALTH Accurate preoperative glioma grading and molecular subtyping are important for treatment. The vascular microe…+ HEALTH Gastrointestinal endoscopy generates extensive high-resolution video data, posing significant challenges for…+ HEALTH Non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) and invasive encapsulat…+ POLICY Artificial intelligence (AI) has the potential to strengthen real-world evidence (RWE) for regulatory decisio…± HEALTH Echocardiography remains the cornerstone of cardiovascular imaging. However, traditional workflows including…
TruaceTracing the truth around AIFriday, August 28, 2026
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877 published stories · page 10 of 59

Evidence-backed gain

Moral distress risk profiling in ICU nurses: A cross-sectional study

BackgroundMoral distress is common among ICU nurses and has been linked to burnout, diminished care quality, and turnover. Which factors matter most - and how they combine to shape individual risk - remains poorly characterised.Research objectiveTo identify factors associated with moral distress in ICU nurses and develop a parsimonious, exploratory risk-profiling model.Research designMulticentre cross-sectional survey with machine learning analysis.Participants and research contextA total of 318 registered nurse…

Nursing Ethics · Labor

Moral distress risk profiling in ICU nurses: A cross-sectional study
Machine learning and multi-omics technologies for precision cardiovascular medicine: advancing diagnosis, risk prediction, and therapeutic guidance
Evidence-backed gain

Machine learning and multi-omics technologies for precision cardiovascular medicine: advancing diagnosis, risk prediction, and therapeutic guidance

Cardiovascular disease remains a major global health burden. Owing to its complex pathogenesis and marked clinical heterogeneity, conventional one-size-fits-all strategies often yield limited benefit for a substantial proportion of patients. Precision medicine advocates individualized management based on patients' clinical and molecular characteristics to improve outcomes. In this context, multi-omics and machine learning provide critical technical support for precision medicine: multi-omics can capture the full…

Health
Machine learning models for predicting neonatal bacterial infections: a retrospective cohort study
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Machine learning models for predicting neonatal bacterial infections: a retrospective cohort study

Bacterial infections represent a critical threat to neonatal health, accounting for approximately 25% of neonatal mortality globally. Timely and precise diagnosis in infants aged 1 to 90 days is essential to facilitate rapid intervention and prevent severe complications. This study aimed to develop and evaluate machine learning (ML) models for the early, non-invasive prediction of bacterial infections using routine clinical data, maximizing clinical interpretability for point-of-care triage. Data from 306 infant…

Health
Beyond Morphology: Reframing Lymph-Node Metastasis Prediction Through Clonal Ecology-Decades-Long Genomic Instability and Polyclonal-to-Monoclonal Transitions as the Missing Dimension in Cancer
Evidence-backed gain

Beyond Morphology: Reframing Lymph-Node Metastasis Prediction Through Clonal Ecology-Decades-Long Genomic Instability and Polyclonal-to-Monoclonal Transitions as the Missing Dimension in Cancer

Recent whole-genome, lineage-tracing, single-cell, and spatial studies have reshaped our understanding of tumor evolution, revealing that cancers can arise from polyclonal populations, undergo decades-long genomic instability before clinical detection, and progress through dynamic changes in subclonal composition, cellular state, and ecological organization. These findings challenge the assumption underlying morphology-based prediction models that metastatic risk can be inferred from static histological features…

Health
Artificial Intelligence in Occlusion-Oriented Digital Reconstruction of Maxillofacial Fractures: Current Applications and Translational Challenges
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Artificial Intelligence in Occlusion-Oriented Digital Reconstruction of Maxillofacial Fractures: Current Applications and Translational Challenges

Maxillofacial fractures require reconstruction of a functional craniofacial unit rather than isolated realignment of fractured bone. Stable occlusion, mandibular movement, temporomandibular joint position, facial contour, and fixation-device adaptation should be considered as interdependent treatment targets. Digital workflows incorporating CT or CBCT reconstruction, virtual surgical planning, CAD/CAM, 3-dimensional printing, patient-specific implants or plates, and navigation have improved visualization and sur…

Health
Artificial Intelligence in Plastic Surgery of the Face: Implications for Esthetic Standards, Patient Perception, and Clinical Practice
Evidence-backed problem

Artificial Intelligence in Plastic Surgery of the Face: Implications for Esthetic Standards, Patient Perception, and Clinical Practice

Artificial intelligence (AI) increasingly influences facial aesthetic standards, alongside the judgment of the surgeon and the goals of the patient. Systems that score, edit, generate, and curate facial images now encode explicit, quantitative definitions of attractiveness, derived from rated image data sets and delivered to the public through attractiveness-prediction algorithms, augmented-reality filters, generative imagery, and surgical-outcome simulators. The following educational review examines how these A…

Health
Transformer-Based Deep Learning Framework for Automated Lesion Detection in Capsule Endoscopy: A Comparative Study With CNN Architectures
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Transformer-Based Deep Learning Framework for Automated Lesion Detection in Capsule Endoscopy: A Comparative Study With CNN Architectures

Goals To compare a vision transformer with 2 convolutional neural network architectures for multiclass lesion classification in capsule endoscopy images. Background Manual review of capsule endoscopy is time-consuming and subject to interobserver variability. Deep learning can automate lesion recognition; however, most prior capsule endoscopy work evaluates a small number of classes, and systematic comparisons between transformer and convolutional architectures across many lesion categories are limited. Study Tw…

Health

Identification of Erectile Dysfunction From Routine Blood Test Data: Development and Validation of a Machine Learning-Based Prediction Model

Background Erectile dysfunction (ED) is a prevalent male health disorder and a recognized sentinel marker for cardiovascular disease. Current diagnostic reliance on subjective questionnaires or invasive examinations limits early screening. Aim We aimed to develop and validate a machine learning model based on routine blood test data to predict ED risk to facilitate its early clinical screening. Methods Data from 4116 men in the NHANES database (2001-2004) formed the training/internal validation sets. An independ…

Health
Identification of Erectile Dysfunction From Routine Blood Test Data: Development and Validation of a Machine Learning-Based Prediction Model

Noise-aware dynamic convolution for improved generalizability of retinal disease diagnosis using optical coherence tomography images

Significance Optical coherence tomography (OCT) is widely used for the diagnosis of retinal diseases. However, deep learning models trained on a single dataset often degrade when deployed across scanners and clinical sites due to device-dependent speckle variability and acquisition differences, limiting their reliability in real-world screening. Aim We aim to develop a lightweight deep learning framework that leverages speckle characteristics in OCT images to improve cross-scanner generalizability for retinal di…

Health
Noise-aware dynamic convolution for improved generalizability of retinal disease diagnosis using optical coherence tomography images

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…

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

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

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

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