Latest AI and machine learning research in clinical trials for healthcare professionals.
Conflicting results from randomized trials regarding the efficacy of remdesivir for COVID-19 have been reported. We aimed to develop a neural network (NN) to identify COVID-19 patients who would derive the greatest survival benefit from remdesivir. This multicenter observational study included adults hospitalized for COVID-19 between February 2020 and February 2021. A derivation cohort from Hospit...
The advent of Large Language Models (LLMs) offers potential solutions to address problems such as shortage of medical resources and low diagnostic consistency in psychiatric clinical practice. Despite this potential, a robust and comprehensive benchmarking framework to assess the efficacy of LLMs in authentic psychiatric clinical environments is absent. This has impeded the advancement of specia...
Traditional autonomous driving systems often struggle to integrate high-level reasoning with low-level control, resulting in suboptimal and sometime...
Ensuring safety in autonomous systems with vision-based control remains a critical challenge due to the high dimensionality of image inputs and the ...
Clinical cohort definition is crucial for patient recruitment and observational studies, yet translating inclusion/exclusion criteria into SQL queri...
Role-playing enables large language models (LLMs) to engage users in immersive and personalized interactions, but it also introduces significant saf...
Foundation Models that are capable of processing and generating multi-modal data have transformed AI's role in medicine. However, a key limitation o...
Purpose: This study aims to develop and validate a method for synthesizing 3D nephrographic phase images in CT urography (CTU) examinations using a ...
Leading vision-language models (VLMs) are trained on general Internet content, overlooking scientific journals' rich, domain-specific knowledge. Tra...
A major challenge to deploying cyber-physical systems with learning-enabled controllers is to ensure their safety, especially in the face of changin...
Large Vision-Language Models (LVLMs) have made significant strides in multimodal comprehension, thanks to extensive pre-training and fine-tuning on ...
Background: Recruitment for cohorts involving complex liver diseases, such as hepatocellular carcinoma and liver cirrhosis, often requires interpret...
Novel research aimed at text-to-image (T2I) generative AI safety often relies on publicly available datasets for training and evaluation, making the...
In the context of blockchain, MEV refers to the maximum value that can be extracted from block production through the inclusion, exclusion, or reord...
Ensuring safety in autonomous driving systems remains a critical challenge, particularly in handling rare but potentially catastrophic safety-critic...
Ensuring the safety of large language models (LLMs) is critical as they are deployed in real-world applications. Existing guardrails rely on rule-ba...
Large Language Models (LLMs) have emerged as powerful tools for addressing modern challenges and enabling practical applications. However, their com...
Despite recent advances in learning-based controllers for legged robots, deployments in human-centric environments remain limited by safety concerns...
Multimodal Large Language Models (MLLMs) have serious security vulnerabilities.While safety alignment using multimodal datasets consisting of text a...
This paper analyzes the safety of Large Language Models (LLMs) in interactions with children below age of 18 years. Despite the transformative appli...