+ HEALTH Aims Intramuscular fat (IMF) is increasingly recognized as a marker of ectopic adiposity and adverse cardiome…+ HEALTH Periapical lesions are challenging to detect on intraoral radiographs because of anatomical superimposition a…± HEALTH Ultrasound imaging is an indispensable diagnostic tool, yet its profound reliance on operator expertise inher…+ HEALTH Objectives We adapted the individualized polysocial risk score (iPsRS), a machine learning model originally d…± HEALTH Melioidosis is a life-threatening infectious disease caused by Burkholderia pseudomallei ( Bp ). Rapid diagno…+ HEALTH Classification is a core machine learning task, and ensemble voting methods are widely used to improve predic…+ CLIMATE Microplastic (MP) pollution poses escalating environmental risks, demanding efficient and reproducible tools…± HEALTH Medicinal chemistry sits at the center of modern drug discovery, yet translating molecular designs into appro…
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Diagnostic accuracy of electronic medical record retrieval methods and a large language model for identifying cardiovascular events: a multisite retrospective validation study in a medical system in the United States

To compare the diagnostic accuracy of four available automated electronic medical record (EMR) retrieval methods, including a large language model (LLM)-assisted workflow, against manual chart adjudication for identifying cardiovascular events. Retrospective diagnostic accuracy study. Three sites within a single US tertiary health system. Two adult cohorts with previously adjudicated cardiovascular outcomes were included. Cohort 1 included 2258 patients treated with immune checkpoint inhibitors, and Cohort 2 inc…

BMJ Open · Health

Diagnostic accuracy of electronic medical record retrieval methods and a large language model for identifying cardiovascular events: a multisite retrospective validation study in a medical system in the United States
Using AI to determine optimal cost-effective diagnostic pathways for chronic breathlessness
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Using AI to determine optimal cost-effective diagnostic pathways for chronic breathlessness

Breathlessness is a common symptom in clinical practice, yet evidence for cost-effective strategies to diagnose the underlying health conditions causing breathlessness remains limited. Using Swedish population data with individuals with moderate to severe breathlessness, we developed an artificial intelligence (AI) reinforcement learning model to identify optimal, low-cost diagnostic pathways for breathlessness tailored to subgroups based on sex and smoking exposure. Sixteen clinically relevant conditions were d…

Health
Trajectory-aware risk stratification of oral lichen planus using a multimodal large language model: a longitudinal diagnostic accuracy study
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Trajectory-aware risk stratification of oral lichen planus using a multimodal large language model: a longitudinal diagnostic accuracy study

To evaluate the performance of a multimodal large language model (LLM) for longitudinal trajectory classification and risk stratification of oral lichen planus (OLP), compared with expert panel consensus. This retrospective diagnostic accuracy study included 300 patients with histopathologically confirmed OLP and at least 24 months of follow-up. Multimodal longitudinal case profiles (serial clinical records, intraoral photographs, and histopathology reports) were independently assessed by (ChatGPT, OpenAI) and a…

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Artificial intelligence for predicting surgical difficulty in laparoscopic cholecystectomy: a systematic review and meta-analysis
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Artificial intelligence for predicting surgical difficulty in laparoscopic cholecystectomy: a systematic review and meta-analysis

Accurately predicting operative difficulty in laparoscopic cholecystectomy (LC) is foundational to personalized surgical planning and patient safety assurance. However, the reliability, generalizability, and true clinical utility of current Artificial Intelligence (AI) models are currently unsubstantiated. This review aimed to evaluate the predictive performance and methodological quality of AI models designed to predict LC surgical difficulty. PubMed, Embase, Web of Science, and the Cochrane Library were search…

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CAUSAL artificial intelligence and data-driven decision intelligence in personalized medicine: a review of healthcare informatics systems
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CAUSAL artificial intelligence and data-driven decision intelligence in personalized medicine: a review of healthcare informatics systems

This review examines the integration of causal artificial intelligence (AI) and data-driven decision intelligence within healthcare informatics systems to advance personalized medicine and clinical decision-making. A narrative review methodology was employed, synthesizing interdisciplinary literature from major databases, including PubMed, Scopus, Web of Science, IEEE Xplore, and ScienceDirect. Studies focusing on causal inference, decision intelligence, and healthcare informatics applications in personalized me…

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

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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…

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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…

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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

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…

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

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…

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

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…

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

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…

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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