Clinical Trials

Automatic construction of risk transmission network about subway construction based on deep learning models.

Safety risks management is a critical part during the subway construction. However, conventional methods for risk identification heavily rely on experience from experts and fail to effectively identify the relationship between risk factors and events embedded in accident texts, which fail to provide substantial guidance for subway safety risks management. With a dataset comprising 562 occurrences ...

Effectiveness of AI-driven interventions in glycemic control: A systematic review and meta-analysis of randomized controlled trials.

This systematic review aims to assess the effectiveness of AI-Driven Decision Support Systems in improving glycemic control, measured by Time in Range (TIR) and HbA1c levels, in patients with diabetes. Included studies were randomized controlled trials (RCTs) that evaluated AI interventions in diabetes management. Exclusion criteria included non-English studies, non-peer-reviewed articles. Studies...

A Computational Approach to Epilepsy Treatment: An AI-optimized Global Natural Product Prescription System

Epilepsy is a prevalent neurological disease with millions of patients worldwide. Many patients have turned to alternative medicine due to the limit...

Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs

Safety-critical applications such as healthcare and autonomous vehicles use deep neural networks (DNN) to make predictions and infer decisions. DNNs...

Virological and Pharmaceutical Properties of Clinically Relevant Phages.

As antimicrobial resistance continues to undermine the efficacy of antibiotics, the global medical community is increasingly turning to alternative tr...

Query-driven Document-level Scientific Evidence Extraction from Biomedical Studies

Extracting scientific evidence from biomedical studies for clinical research questions (e.g., Does stem cell transplantation improve quality of life...

AI-Driven Advances in Parkinson's Disease Neurosurgery: Enhancing Patient Selection, Trial Efficiency, and Therapeutic Outcomes.

Parkinson's disease (PD) is a progressive neurodegenerative disorder marked by motor and non-motor dysfunctions that severely compromise patients' qua...

Transformer-Based Language Models for Group Randomized Trial Classification in Biomedical Literature: Model Development and Validation.

BACKGROUND: For the public health community, monitoring recently published articles is crucial for staying informed about the latest research developm...

Concordance with SPIRIT-AI guidelines in reporting of randomized controlled trial protocols investigating artificial intelligence in oncology: a systematic review.

BACKGROUND: Artificial intelligence (AI) is a promising tool used in oncology that may be able to facilitate diagnosis, treatment planning, and patien...

Predicting the efficacy of bevacizumab on peritumoral edema based on imaging features and machine learning.

This study proposes a novel approach to predict the efficacy of bevacizumab (BEV) in treating peritumoral edema in metastatic brain tumor patients by ...

REVEAL: Multi-turn Evaluation of Image-Input Harms for Vision LLM

Vision Large Language Models (VLLMs) represent a significant advancement in artificial intelligence by integrating image-processing capabilities wit...

The Aloe Family Recipe for Open and Specialized Healthcare LLMs

Purpose: With advancements in Large Language Models (LLMs) for healthcare, the need arises for competitive open-source models to protect the public ...

Sparsity is All You Need: Rethinking Biological Pathway-Informed Approaches in Deep Learning

Biologically-informed neural networks typically leverage pathway annotations to enhance performance in biomedical applications. We hypothesized that...

With Friends Like These, Who Needs Explanations? Evaluating User Understanding of Group Recommendations

Group Recommender Systems (GRS) employing social choice-based aggregation strategies have previously been explored in terms of perceived consensus, ...

Unmasking the Canvas: A Dynamic Benchmark for Image Generation Jailbreaking and LLM Content Safety

Existing large language models (LLMs) are advancing rapidly and produce outstanding results in image generation tasks, yet their content safety chec...

Data-Driven Falsification of Cyber-Physical Systems

Cyber-Physical Systems (CPS) are abundant in safety-critical domains such as healthcare, avionics, and autonomous vehicles. Formal verification of t...

Integrating generative AI and machine learning classifiers for solving heterogenous MCGDM: a case of employee churn prediction.

Employee churn is a critical issue for companies and organizations, as it directly impacts productivity, efficiency, and overall operational success. ...

Uncertainty Quantification for Machine Learning in Healthcare: A Survey

Uncertainty Quantification (UQ) is pivotal in enhancing the robustness, reliability, and interpretability of Machine Learning (ML) systems for healt...

Closed-loop control of seizure activity via real-time seizure forecasting by reservoir neuromorphic computing

Closed-loop brain stimulation holds potential as personalized treatment for drug-resistant epilepsy (DRE) but still suffers from limitations that re...

LLM-based Text Simplification and its Effect on User Comprehension and Cognitive Load

Information on the web, such as scientific publications and Wikipedia, often surpasses users' reading level. To help address this, we used a self-re...