Latest AI and machine learning research in pain management for healthcare professionals.
While recent advancements in multimodal language models have enabled image generation from expressive multi-image instructions, existing methods struggle to maintain performance under complex interleaved instructions. This limitation stems from the structural separation of images and text in current paradigms, which forces models to bridge difficult long-range dependencies to match descriptions wi...
Background: Large language models (LLMs) are increasingly used in medical education and clinical decision-making, but their reliability in high-risk medication dosing remains unclear. Opioid rotation is a common task requiring precise calculations where errors may result in overdose or inadequate pain relief. Methods: Thirteen LLMs were tested using an API-based framework to ensure independent que...
Opioid addiction is characterized by escalating drug use, driven in part by negative reinforcement from withdrawal, but the neural processes linking w...
The lack of analytical models describing diffusion time dependence at intermediate time scales in complex tissue microstructure limits the accurate qu...
Machine learning models that can utilize high-dimensional data to make predictions and derive biological insights can improve understanding of disease...
Background and Objectives Preoperative prediction of functional outcomes in contrast-enhancing glioma could support surgical decision-making and patie...
Accurate intraoperative assessment of glioma infiltration is essential for maximizing tumor resection while preserving functional brain tissue. Fluore...
Unaddressed pain in neonates can lead to adverse effects, including delayed development and slower weight gain, emphasising the need for more objectiv...
Automatic depression detection from conversational interactions holds significant promise for scalable screening but remains hindered by severe data s...
Single image dehazing is often constrained by a trade-off between restoration quality and computational efficiency. While efficient, CNN networks stru...
Introduction Large language models are increasingly being used in healthcare. In interventional pain medicine, clinical reasoning is essential for pro...
Medical Visual Question Answering (MedVQA) aims to generate clinically reliable answers conditioned on complex medical images and questions. However, ...
Underwater images often suffer from severe degradation, such as color distortion, low contrast, and blurred details, due to light absorption and scatt...
Effective abstention (EA), recognizing evidence insufficiency and refraining from answering, is critical for reliable multimodal systems. Yet existing...
Understanding how neuronal circuits transform inputs into outputs requires systematic perturbation under controlled conditions. In vitro neuronal netw...
Background: The urgent care departments in Europe face a structural paradox: accelerating digitalisation is accompanied by a patient population that i...
Unified multimodal models (UMMs) were designed to combine the reasoning ability of large language models (LLMs) with the generation capability of visi...
Recent multimodal large language models (MLLMs) have begun to support Thinking with Images by invoking visual tools such as zooming and cropping durin...
In modern cloud and heterogeneous distributed infrastructures, container images are widely used as the deployment unit for machine learning applicatio...
Vision-Language Models (VLMs) are powerful but remain vulnerable to multimodal jailbreak attacks. Existing attacks mainly rely on either explicit visu...