Latest AI and machine learning research in clinical trials for healthcare professionals.
Lysergic acid diethylamide (LSD) profoundly alters conscious experience, yet the electrophysiologica...
With the rapid development of industrial intelligence and unmanned inspection, reliable perception a...
Meta reinforcement learning (RL) allows agents to leverage experience across a distribution of tasks...
The rise of AI agents introduces complex safety and security challenges arising from autonomous tool...
Patient selection and enrolment into phase III randomized clinical trials (RCTs) of adjuvant cyclin-...
The rapid advancement of Multimodal Large Language Models (MLLMs) has introduced complex security ch...
Importance: Emerging evidence suggests healthcare AI systems may exhibit deceptive alignment (appear...
As Multimodal Large Language Models (MLLMs) acquire stronger reasoning capabilities to handle comple...
Background and Aims: Pragmatic clinical trials are designed to assess interventions in real-world se...
In this paper, the CD-TWINSAFE is introduced, a V2I-based digital twin for Autonomous Vehicles. The ...
The rapid integration of AI algorithms in safety-critical applications such as autonomous driving an...
Background/ObjectivesHead and neck cancer (HNC) represents the seventh most common cancer diagnosis ...
The rapid evolution of Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) has...
Sustainability is becoming increasingly critical in the maritime transport, encompassing both enviro...
ObjectiveLarge language models (LLMs) are increasingly embedded in mental-health chatbots, yet safe ...
Currently, chemotherapy drugs are the first-line treatment for lung cancer patients, and evaluating ...
Neural networks' insufficient interpretability can lead to unguaranteed Safety of the Intended Funct...
Large language model (LLM) chatbots show increasing promise in persuasive communication. Yet their...
We propose Adaptive Diffusion Denoised Smoothing, a method for certifying the predictions of a vis...
We propose a novel multi-task neural network approach for estimating distributional treatment effe...
The Average Treatment Effect (ATE) is a foundational metric in causal inference, widely used to as...