Latest AI and machine learning research in clinical trials for healthcare professionals.
The domains of food safety, quality, and nutrition are inundated with complex datasets. Machine learning (ML) has emerged as a powerful tool in food science, offering fast, accessible, and effective solutions compared with conventional methods. This review outlines the applications of ML in safeguarding food safety, enhancing quality, and unraveling nutrition intricacies. The review encompasses th...
Multimodal Large Language Models (MLLMs) have enabled transformative advancements across diverse applications but remain susceptible to safety threats, especially jailbreak attacks that induce harmful outputs. To systematically evaluate and improve their safety, we organized the Adversarial Testing & Large-model Alignment Safety Grand Challenge (ATLAS) 2025}. This technical report presents findi...
One of the major challenges in estimating conditional potential outcomes and conditional average treatment effects (CATE) is the presence of hidden ...
In randomized clinical trials, regression models can be used to explore the relationships between patients' variables (e.g., clinical, pathological ...
In the past, the development of vaccines and immunotherapeutics relied heavily on trial-and-error experimentation and extensive in vivo testing, oft...
High-dose-rate (HDR) brachytherapy plays a critical role in the treatment of locally advanced cervical cancer but remains highly dependent on manual...
LLM-based Conversational AIs (CAIs), also known as GenAI chatbots, like ChatGPT, are increasingly used across various domains, but they pose privacy...
Claims made by individuals or entities are oftentimes nuanced and cannot be clearly labeled as entirely "true" or "false" -- as is frequently the ca...
Large Vision-Language Models (LVLMs) have achieved impressive progress across various applications but remain vulnerable to malicious queries that e...
With the increasing availability of aerial and satellite imagery, deep learning presents significant potential for transportation asset management, ...
The recent emergence of multimodal large language models (LLMs) has introduced new opportunities for improving visual hazard recognition on construc...
In complex driving environments, autonomous vehicles must navigate safely. Relying on a single predicted path, as in regression-based approaches, us...
The application scope of Large Language Models (LLMs) continues to expand, leading to increasing interest in personalized LLMs that align with human...
Despite emerging efforts to enhance the safety of Vision-Language Models (VLMs), current approaches face two main shortcomings. 1) Existing safety-t...
Despite emerging efforts to enhance the safety of Vision-Language Models (VLMs), current approaches face two main shortcomings. 1) Existing safety-t...
The rapid progress of generative AI has enabled remarkable creative capabilities, yet it also raises urgent concerns regarding the safety of AI-gene...
Trustworthiness in healthcare question-answering (QA) systems is important for ensuring patient safety, clinical effectiveness, and user confidence....
Providing effective treatment and making informed clinical decisions are essential goals of modern medicine and clinical care. We are interested in ...
Urban and transportation research has long sought to uncover statistically meaningful relationships between key variables and societal outcomes such...
Traditional Chinese Medicine (TCM) is a holistic medical system with millennia of accumulated clinical experience, playing a vital role in global he...