Latest AI and machine learning research in bioterrorism for healthcare professionals.
Text-to-image (T2I) diffusion models have achieved remarkable success in image synthesis, but their reliance on large-scale data and open ecosystems introduces serious backdoor security risks. Existing defenses, particularly input-level methods, are more practical for deployment but often rely on observable anomalies that become unreliable under stealthy, semantics-preserving trigger designs. As m...
We present a discriminative multimodal reward model that scores all candidate responses in a single forward pass. Conventional discriminative reward models evaluate each response independently, requiring multiple forward passes, one for each potential response. Our approach concatenates multiple responses with separator tokens and applies cross-entropy over their scalar scores, enabling direct com...
Purpose. High-grade serous ovarian carcinoma (HGSOC) is characterized by pronounced biological and spatial heterogeneity and is frequently diagnosed a...
Two problems often plague medical imaging analysis: 1) Non-availability of large quantities of labeled training data, and 2) Dealing with imbalanced d...
Predicting drug response in patients from preclinical data remains a major challenge in precision oncology due to the substantial biological gap betwe...
Real-time tracking and automated response systems are essential for standardising experiments, reducing observer bias, and improving reproducibility i...
Objective Cognitive behavioral therapy (CBT) is an effective first-line treatment for obsessive-compulsive disorder (OCD), yet it remains difficult to...
Background Timely detection of seasonal influenza outbreaks is critical for healthcare system preparedness and public health response. Although numero...
Hospital artificial intelligence (AI) and robotics are spreading unevenly across the United States, yet national evidence on how these technologies ar...
Large language model (LLM)-based AI systems have shown promise for patient-facing diagnostic and management conversations in simulated settings. Trans...
Large language model (LLM)-based AI systems have shown promise for patient-facing diagnostic and management conversations in simulated settings. Trans...
Visual Question Answering (VQA) is a core task for evaluating the capabilities of Vision-Language Models (VLMs). Existing VQA benchmarks primarily fea...
Background Artificial intelligence (AI) is increasingly being integrated into healthcare systems, with growing applications in clinical decision suppo...
Background: Nontyphoidal Salmonella enterica (NTS) is a major public-health threat in the United States of America (U.S.). Evaluating associations bet...
The SARS-CoV-2 Delta variant (B.1.617.2), initially classified as a variant of concern due to its enhanced transmissibility and vaccine-escape mutatio...
Hypothalamic-pituitary-adrenal axis (HPA axis) dysregulation is a risk factor for poor mental and physical health. Animal studies indicate that DNA me...
Accurately predicting chemotherapy response remains a major challenge in precision oncology. Although machine-learning models based on tumour omics da...
Background: Large language models (LLMs) show promise for clinical decision support, yet most validation studies evaluate single models, leaving quest...
Assessing the human infection potential of emerging coronaviruses remains a critical challenge for global health preparedness. In this study, we devel...
Background: Large language models (LLMs) are increasingly piloted as chat interfaces for chart review and clinical decision support. Although leading ...