Product Alert

Predicting cognitive-behavioral therapy outcomes in obsessive-compulsive disorder from inhibitory control neural activity: A mega-analysis and machine learning study from the ENIGMA-OCD consortium

Objective Cognitive behavioral therapy (CBT) is an effective first-line treatment for obsessive-compulsive disorder (OCD), yet it remains difficult to predict who will respond to this intervention. This study investigates associations between neural activity during inhibitory control tasks and CBT outcomes, and whether task-based fMRI data could serve as a predictive marker of individual CBT respo...

Forecasting Epileptic Seizures from Contactless Camera via Cross-Species Transfer Learning

Epileptic seizure forecasting is a clinically important yet challenging problem in epilepsy research. Existing approaches predominantly rely on neural signals such as electroencephalography (EEG), which require specialized equipment and limit long-term deployment in real-world settings. In contrast, video data provide a non-invasive and accessible alternative, yet existing video-based studies main...

Managing Cognitive Bias in Human Labeling Operations for Rare-Event AI: Evidence from a Field Experiment

Many operational AI systems depend on large-scale human annotation to detect rare but consequential events (e.g., fraud, defects, and medical abnormal...

mRNA-Protein Coordination is Contextualized by Metastatic Biological Phenotypes

A central goal of conducting omics measurements is to understand how molecular features inform higher-order cell- and tissue-level phenotypes. In part...

Why Does It Look There? Structured Explanations for Image Classification

Deep learning models achieve remarkable predictive performance, yet their black-box nature limits transparency and trustworthiness. Although numerous ...

A prospective clinical feasibility study of a conversational diagnostic AI in an ambulatory primary care clinic

Large language model (LLM)-based AI systems have shown promise for patient-facing diagnostic and management conversations in simulated settings. Trans...

Synthetic Defect Image Generation for Power Line Insulator Inspection Using Multimodal Large Language Models

Utility companies increasingly rely on drone imagery for post-event and routine inspection, but training accurate defect-type classifiers remains diff...

A prospective clinical feasibility study of a conversational diagnostic AI in an ambulatory primary care clinic

Large language model (LLM)-based AI systems have shown promise for patient-facing diagnostic and management conversations in simulated settings. Trans...

Interpretable Aneurysm Classification via 3D Concept Bottleneck Models: Integrating Morphological and Hemodynamic Clinical Features

We are concerned with the challenge of reliably classifying and assessing intracranial aneurysms using deep learning without compromising clinical tra...

Med-Evo: Test-time Self-evolution for Medical Multimodal Large Language Models

Medical Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities across diverse healthcare tasks. However, current post-trai...

GRD-Net: Generative-Reconstructive-Discriminative Anomaly Detection with Region of Interest Attention Module

Anomaly detection is nowadays increasingly used in industrial applications and processes. One of the main fields of the appliance is the visual inspec...

Trustworthy personalized treatment selection: causal effect-trees and calibration in perioperative medicine

Background Personalized medicine promises to tailor treatments to the individual, but it carries a hidden risk: mistaking statistical noise for action...

AI-Generated Responses to Patient's Messages: Effectiveness, Feasibility and Implementation

Background Generative artificial intelligence (GenAI) in healthcare may reduce administrative burden and enhance quality of care. Large language model...

The Causal Impact of Natural Language Processing-Driven Clinical Decision Support on Sepsis Mortality in England: An Augmented Synthetic Control Analysis of NHS Trust-Level Data

Background: Sepsis remains a leading cause of preventable hospital mortality in England, with NHS England reporting over 48,000 sepsis-related deaths ...

vToxiNet: a biologically constrained deep learning framework for interpretable prediction of drug-induced hepatotoxicity

Hepatotoxicity remains a leading cause of drug attrition and post-marketing withdrawal, resulting from diverse and complex toxicity mechanisms. Tradit...

Compensation-free Machine Unlearning in Text-to-Image Diffusion Models by Eliminating the Mutual Information

The powerful generative capabilities of diffusion models have raised growing privacy and safety concerns regarding generating sensitive or undesired c...

Fed-ADE: Adaptive Learning Rate for Federated Post-adaptation under Distribution Shift

Federated learning (FL) in post-deployment settings must adapt to non-stationary data streams across heterogeneous clients without access to ground-tr...

When Does RL Help Medical VLMs? Disentangling Vision, SFT, and RL Gains

Reinforcement learning (RL) is increasingly used to post-train medical Vision-Language Models (VLMs), yet it remains unclear whether RL improves medic...

Neural Image Space Tessellation

We present Neural Image-Space Tessellation (NIST), a lightweight screen-space post-processing approach that produces the visual effect of tessellated ...

Interpretable Debiasing of Vision-Language Models for Social Fairness

The rapid advancement of Vision-Language models (VLMs) has raised growing concerns that their black-box reasoning processes could lead to unintended f...