Public Health & Policy

Clinical Trials

Latest AI and machine learning research in clinical trials for healthcare professionals.

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Computational Safety for Generative AI: A Signal Processing Perspective

AI safety is a rapidly growing area of research that seeks to prevent the harm and misuse of front...

Can't See the Forest for the Trees: Benchmarking Multimodal Safety Awareness for Multimodal LLMs

Multimodal Large Language Models (MLLMs) have expanded the capabilities of traditional language mo...

SafeDialBench: A Fine-Grained Safety Benchmark for Large Language Models in Multi-Turn Dialogues with Diverse Jailbreak Attacks

With the rapid advancement of Large Language Models (LLMs), the safety of LLMs has been a critical...

VLM-Guard: Safeguarding Vision-Language Models via Fulfilling Safety Alignment Gap

The emergence of vision language models (VLMs) comes with increased safety concerns, as the incorp...

Are Smarter LLMs Safer? Exploring Safety-Reasoning Trade-offs in Prompting and Fine-Tuning

Large Language Models (LLMs) have demonstrated remarkable success across various NLP benchmarks. H...

Generation of Drug-Induced Cardiac Reactions towards Virtual Clinical Trials

Clinical trials remain critical in cardiac drug development but face high failure rates due to eff...

ELITE: Enhanced Language-Image Toxicity Evaluation for Safety

Current Vision Language Models (VLMs) remain vulnerable to malicious prompts that induce harmful o...

AIQViT: Architecture-Informed Post-Training Quantization for Vision Transformers

Post-training quantization (PTQ) has emerged as a promising solution for reducing the storage and ...

Efficient Randomized Experiments Using Foundation Models

Randomized experiments are the preferred approach for evaluating the effects of interventions, but...

Randomized and Inner-product Free Krylov Methods for Large-scale Inverse Problems

Iterative Krylov projection methods have become widely used for solving large-scale linear inverse...

INTACT: Inducing Noise Tolerance through Adversarial Curriculum Training for LiDAR-based Safety-Critical Perception and Autonomy

In this work, we present INTACT, a novel two-phase framework designed to enhance the robustness of...

Safety at Scale: A Comprehensive Survey of Large Model Safety

The rapid advancement of large models, driven by their exceptional abilities in learning and gener...

Generalizing Safety Beyond Collision-Avoidance via Latent-Space Reachability Analysis

Hamilton-Jacobi (HJ) reachability is a rigorous mathematical framework that enables robots to simu...

Effect of Laminectomy Methods on the Surgical Safety of Automatic Laminectomy Robot.

BACKGROUND: The efficacy of laminectomy procedures is contingent on the method of resection. The obj...

Feb 2025 39815793
AFFIPred: AlphaFold2 structure-based Functional Impact Prediction of missense variations.

Protein structure holds immense potential for pathogenicity prediction, albeit structure-based predi...

Feb 2025 39840793
Rethinking Bottlenecks in Safety Fine-Tuning of Vision Language Models

Large Vision-Language Models (VLMs) have achieved remarkable performance across a wide range of ta...

Towards Safe AI Clinicians: A Comprehensive Study on Large Language Model Jailbreaking in Healthcare

Large language models (LLMs) are increasingly utilized in healthcare applications. However, their ...

Beyond Benchmarks: On The False Promise of AI Regulation

The rapid advancement of artificial intelligence (AI) systems in critical domains like healthcare,...

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