Clinical Trials

Clinical implications of deep learning based image analysis of whole radical prostatectomy specimens.

Prostate cancer (PCa) diagnosis faces significant challenges due to its complex pathological characteristics and insufficient pathologist resources. While deep learning-based image analysis (DLIA) shows promise in enhancing diagnostic accuracy, its application to radical prostatectomy (RP) specimens remains underexplored. In this study, we evaluated the clinical feasibility and prognostic value of...

Unveiling drug-induced osteotoxicity: A machine learning approach and webserver.

Drug-induced osteotoxicity refers to the harmful effects certain pharmaceuticals have on the skeletal system, posing significant safety risks. These toxic effects are critical concerns in clinical practice, drug development, and environmental management. However, current toxicity assessment models lack specialized datasets and algorithms specifically designed to predict osteotoxicity In this study...

A Framework for Parameter Estimation and Uncertainty Quantification in Systems Biology Using Quantile Regression and Physics-Informed Neural Networks.

A framework for parameter estimation and uncertainty quantification is crucial for understanding the mechanisms of biological interactions within comp...

AI-Enabled IoT for Food Computing: Challenges, Opportunities, and Future Directions.

Food computing refers to the integration of digital technologies, such as artificial intelligence (AI), the Internet of Things (IoT), and data-driven ...

scCorrect: Cross-modality label transfer from scRNA-seq to scATAC-seq using domain adaptation.

Cell type annotation in single-cell chromatin accessibility sequencing (scATAC-seq) is crucial for enabling researchers to identify subpopulations of ...

MultiCTox: Empowering Accurate Cardiotoxicity Prediction through Adaptive Multimodal Learning.

Cardiotoxicity refers to the inhibitory effects of drugs on cardiac ion channels. Accurate prediction of cardiotoxicity is crucial yet challenging, as...

Exploring the Potential of Large Language Models for Automated Safety Plan Scoring in Outpatient Mental Health Settings.

The Safety Planning Intervention (SPI) produces a plan to help manage patients' suicide risk. High-quality safety plans - that is, those with greater ...

A Machine Learning Approach to Predict Cognitive Decline in Alzheimer Disease Clinical Trials.

BACKGROUND AND OBJECTIVES: Among the participants of Alzheimer disease (AD) treatment trials, 40% do not show cognitive decline over 80 weeks of follo...

The AI-environment paradox: Unraveling the impact of artificial intelligence (AI) adoption on pro-environmental behavior through work overload and self-efficacy in AI learning.

This study examines the complex relationships among artificial intelligence (AI) adoption in organizations, employee work overload, and pro-environmen...

Development of an Intuitive Interface With Haptic Enhancement for Robot-Assisted Endovascular Intervention.

Robot-assisted endovascular intervention has the potential to reduce radiation exposure to surgeons and enhance outcomes of interventions. However, th...

Integrating machine learning and a large language model to construct a domain knowledge graph for reducing the risk of fall-from-height accidents.

Fall-from-height (FFH) accidents remain a major source of workplace injuries and fatalities. Fall protection systems (FPS) are critical for preventing...

Artificial Intelligence-Guided Bronchoscopy is Superior to Human Expert Instruction for the Performance of Critical-Care Physicians: A Randomized Controlled Trial.

OBJECTIVES: Bronchoscopy in the mechanically ventilated patient is an important skill for critical-care physicians. However, training opportunity is h...

American College of Veterinary Radiology and European College of Veterinary Diagnostic Imaging position statement on artificial intelligence.

The American College of Veterinary Radiology (ACVR) and the European College of Veterinary Diagnostic Imaging (ECVDI) recognize the transformative pot...

Improving Dietary Supplement Information Retrieval: Development of a Retrieval-Augmented Generation System With Large Language Models.

BACKGROUND: Dietary supplements (DSs) are widely used to improve health and nutrition, but challenges related to misinformation, safety, and efficacy ...

A randomized controlled trial of the effects of dog-assisted versus robot dog-assisted therapy for children with autism or Down syndrome.

Research with controlled or crossover designs in animal-assisted therapy have largely used control groups receiving no treatment or treatment as usual...

Developing a deep learning model for the automated monitoring of acupuncture needle insertion: enhancing safety in traditional acupuncture practices.

BACKGROUND: Acupuncture is a widely practiced traditional therapy, yet safety concerns, particularly needle breakage and retention, remain critical is...

Effectiveness of Chatbot interventions for reducing caregiver burden: Protocol for a systematic review and meta-analysis.

This protocol outlines a systematic review and meta-analysis examining the effectiveness of fully automated, AI-driven chatbot interventions in reduci...

Progress in machine learning-supported electronic nose and hyperspectral imaging technologies for food safety assessment: A review.

The growing concern over food safety, driven by threats such as food contaminations and adulterations has prompted the adoption of advanced technologi...

State-of-the-art for automated machine learning predicts outcomes in poor-grade aneurysmal subarachnoid hemorrhage using routinely measured laboratory & radiological parameters: coagulation parameters and liver function as key prognosticators.

The objective of this study was to develop and evaluate automated machine learning (aML) models for predicting short-term (1-month) and medium-term (3...

Evaluating the Performance and Safety of Large Language Models in Generating Type 2 Diabetes Mellitus Management Plans: A Comparative Study With Physicians Using Real Patient Records.

Background The integration of large language models (LLMs) such as GPT-4 into healthcare presents potential benefits and challenges. While LLMs show p...