Artificial intelligence-driven design of next-generation nanomedicine: transforming precision healthcare.

Counterfactual Diffusion Models Provide Interpretable Explanations of Artificial Intelligence Models in Pathology.

UNLABELLED: Deep learning can extract predictive and prognostic biomarkers from histopathology whole-slide images. However, explainable artificial intelligence approaches widely used in digital pathol...

Admission-Based Machine-Learning Models for Predicting Mechanical Ventilation and Mortality in Fibrotic Interstitial Lung Disease: A Multicenter Cohort Study with External Validation.

BACKGROUND: Admission-based risk stratification tools are limited for hospitalized patients with fibrotic interstitial lung disease (F-ILD). AIMS: To develop and externally validate admission-based ma...

A unified approach for maintaining MRI reconstruction quality and quantifying both aleatoric and epistemic uncertainty.

This study investigates practical design choices for Bayesian uncertainty quantification (UQ) in model-based deep learning (MoDL) for accelerated MRI reconstruction. Specifically, we examine where to ...

Artificial intelligence in pain assessment and management for older adults: A scoping review.

INTRODUCTION: Conventional pain management strategies often fall short in addressing the complex needs of older adults with pain. Artificial intelligence (AI) represents a significant potential for ad...

MedP-CLIP: Medical CLIP with region-aware prompt integration.

Contrastive Language-Image Pre-training (CLIP) has demonstrated outstanding performance in global image understanding and zero-shot transfer through large-scale text-image alignment. However, the core...

INTELCAPE: A Deep Learning-Powered System for Automated, High-Accuracy Crohn's Disease Diagnosis via Capsule Endoscopy.

BACKGROUND & AIMS: Capsule endoscopy (CE) is a noninvasive technique for diagnosing Crohn's disease (CD); however, manual interpretation of CE videos is time-consuming and error-prone. We developed an...

Disease and Health Surveillance in Companion Animals Using Artificial Intelligence and Machine Learning.

Companion animal disease surveillance now benefits from collated databases of electronic health records and artificial intelligence. This review examines computational approaches for analyzing unstruc...

AI-Driven Atrial Fibrillation Management: From Signal to Strategy.

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia worldwide and is associated with substantial morbidity, including ischemic stroke, heart failure, and cognitive decline. Despit...

Advancing polypharmacology-based drug discovery using deep generative models.

Artificial intelligence for detection, grading, and prognostication in prostate cancer pathology: A scoping review.

Artificial intelligence (AI) has been transforming many aspects of medical care. In prostate cancer, ongoing progress in AI has improved research and patient care. Recent advances in machine learning ...

Deep integration of clinical metadata with [

PURPOSE: To develop and validate a multimodal deep learning framework that integrates clinical metadata with [ METHODS: A multi-center surgical NSCLC cohort (n = 780) was partitioned into a developmen...

MATCH: client-therapist matching with machine learning.

OBJECTIVE: Therapist assignment can have a profound impact on the success of counseling. Accordingly, we present a framework that combines archival data and computer science algorithms to match client...

Two decades and beyond: What we know and still need to learn about the TWEAK/Fn14 pathway.

TWEAK is a multifunctional cytokine that exists in both membrane-bound and soluble forms. TWEAK interacts with its sole receptor, Fn14, leading to the activation of various downstream signaling pathwa...

Incubating artificial intelligence initiatives and careers in dermatology.

Spatial radiomics-based interpretable multimodal machine learning model enhances outcomes prediction for minor stroke: A multicenter cohort study.

INTRODUCTION: Accurately identifying unfavorable outcomes is crucial for the clinical management of minor stroke. Conventional imaging prediction models, such as radiomics, rely on region-of-interest ...

Optimized Multi-Contrast Self-Supervised MRI Reconstruction Using Learned K-Space Partitioning.

OBJECTIVE: Deep Learning has shown promise in accelerating MRI by reconstructing high-quality images from under-sampled data. While recent work has leveraged multi-contrast information to improve reco...

THRIVE: A Phase 3, Randomized, Double-Masked, Placebo-Controlled Study of Veligrotug for Active Thyroid Eye Disease.

PURPOSE: Thyroid eye disease (TED) is a debilitating autoimmune disease with significant unmet needs. This study's objective was to assess the efficacy and safety of veligrotug, a full antagonist mono...

Applying clinical natural language processing to lung cancer in Spain: a terminology-based panel approach.

BACKGROUND: We present a novel methodological framework for developing and validating a terminology-based Disease Panel to identify and characterize patients with metastatic non-small-cell lung cancer...

[Use of artificial intelligence in clinical practice and hospitals].

Artificial intelligence (AI) is increasingly evolving from a research technology into a tool for everyday clinical practice. While early applications primarily focused on medical image analysis, gener...