Clinical Trials

Am I eligible? Natural Language Inference for Clinical Trial Patient Recruitment: the Patient's Point of View

Recruiting patients to participate in clinical trials can be challenging and time-consuming. Usually, participation in a clinical trial is initiated by a healthcare professional and proposed to the patient. Promoting clinical trials directly to patients via online recruitment might help to reach them more efficiently. In this study, we address the case where a patient is initiating their own rec...

Real-world validation of a multimodal LLM-powered pipeline for High-Accuracy Clinical Trial Patient Matching leveraging EHR data

Background: Patient recruitment in clinical trials is hindered by complex eligibility criteria and labor-intensive chart reviews. Prior research using text-only models have struggled to address this problem in a reliable and scalable way due to (1) limited reasoning capabilities, (2) information loss from converting visual records to text, and (3) lack of a generic EHR integration to extract pat...

Towards Understanding the Safety Boundaries of DeepSeek Models: Evaluation and Findings

This study presents the first comprehensive safety evaluation of the DeepSeek models, focusing on evaluating the safety risks associated with their ...

MMDT: Decoding the Trustworthiness and Safety of Multimodal Foundation Models

Multimodal foundation models (MMFMs) play a crucial role in various applications, including autonomous driving, healthcare, and virtual assistants. ...

Construction Site Scaffolding Completeness Detection Based on Mask R-CNN and Hough Transform

Construction site scaffolding is essential for many building projects, and ensuring its safety is crucial to prevent accidents. The safety inspector...

MFP-CLIP: Exploring the Efficacy of Multi-Form Prompts for Zero-Shot Industrial Anomaly Detection

Recently, zero-shot anomaly detection (ZSAD) has emerged as a pivotal paradigm for identifying defects in unseen categories without requiring target...

Hyperbolic Safety-Aware Vision-Language Models

Addressing the retrieval of unsafe content from vision-language models such as CLIP is an important step towards real-world integration. Current eff...

Safety Mirage: How Spurious Correlations Undermine VLM Safety Fine-tuning

Recent vision-language models (VLMs) have made remarkable strides in generative modeling with multimodal inputs, particularly text and images. Howev...

Autoregressive Image Generation with Randomized Parallel Decoding

We introduce ARPG, a novel visual autoregressive model that enables randomized parallel generation, addressing the inherent limitations of conventio...

Enhance Exploration in Safe Reinforcement Learning with Contrastive Representation Learning

In safe reinforcement learning, agent needs to balance between exploration actions and safety constraints. Following this paradigm, domain transfer ...

Optimizing Fire Safety: Reducing False Alarms Using Advanced Machine Learning Techniques

Fire safety practices are important to reduce the extent of destruction caused by fire. While smoke alarms help save lives, firefighters struggle wi...

A Novel Framework for Comparing Combination Therapy Outcomes Using Mechanistic Graph Models

Background: Predicting the efficacy of combination therapies is a critical challenge in clinical decision-making, particularly for diseases requirin...

How good are deep learning methods for automated road safety analysis using video data? An experimental study

Image-based multi-object detection (MOD) and multi-object tracking (MOT) are advancing at a fast pace. A variety of 2D and 3D MOD and MOT methods ha...

Efficient dynamic modal load reconstruction using physics-informed Gaussian processes based on frequency-sparse Fourier basis functions

Knowledge of the force time history of a structure is essential to assess its behaviour, ensure safety and maintain reliability. However, direct mea...

Towards Regulatory-Confirmed Adaptive Clinical Trials: Machine Learning Opportunities and Solutions

Randomized Controlled Trials (RCTs) are the gold standard for evaluating the effect of new medical treatments. Treatments must pass stringent regula...

Generating Robot Constitutions & Benchmarks for Semantic Safety

Until recently, robotics safety research was predominantly about collision avoidance and hazard reduction in the immediate vicinity of a robot. Sinc...

Real-Time Structural Deflection Estimation in Hydraulically Actuated Systems Using 3D Flexible Multibody Simulation and DNNs

The precision, stability, and performance of lightweight high-strength steel structures in heavy machinery is affected by their highly nonlinear dyn...

AttentionSwarm: Reinforcement Learning with Attention Control Barier Function for Crazyflie Drones in Dynamic Environments

We introduce AttentionSwarm, a novel benchmark designed to evaluate safe and efficient swarm control across three challenging environments: a landin...

Benchmarking Chinese Medical LLMs: A Medbench-based Analysis of Performance Gaps and Hierarchical Optimization Strategies

The evaluation and improvement of medical large language models (LLMs) are critical for their real-world deployment, particularly in ensuring accura...

Evaluation of Safety Cognition Capability in Vision-Language Models for Autonomous Driving

Assessing the safety of vision-language models (VLMs) in autonomous driving is particularly important; however, existing work mainly focuses on trad...