+ 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…
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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…

Frontiers in Cardiovascular Medicine · Health

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

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

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

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

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

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