Latest AI and machine learning research in pain management for healthcare professionals.
Multimodal Large Language Models (MLLMs) deliver detailed responses on vision-language tasks, yet remain susceptible to object hallucination (introducing objects not present in the image), undermining reliability in practice. Prior efforts often rely on heuristic penalties, post-hoc correction, or generic decoding tweaks, which do not directly intervene in the mechanisms that trigger object halluc...
Background: Diagnostic errors are a leading cause of preventable patient harm, often occurring during early clinical encounters where diagnostic uncertainty is maximal. Large language models (LLMs) have shown potential in medical reasoning, yet their ability to function as a diagnostic safety net, specifically by identifying and correcting human diagnostic errors, remains systematically unquantifi...
Counterfactual inference enables clinicians to ask "what if" questions about patient outcomes, but standard methods assume feature independence and si...
Unified multimodal models can both understand and generate visual content within a single architecture. Existing models, however, remain data-hungry a...
Temporal information in structured electronic health records (EHRs) is often lost in sparse one-hot or count-based representations, while sequence mod...
Unified multimodal models can both understand and generate visual content within a single architecture. Existing models, however, remain data-hungry a...
Background Tinnitus affects a substantial proportion of the global population and can severely disrupt sleep, mood, and daily functioning, yet the qua...
Abstract Background Neurodegenerative diseases, including Alzheimer's disease (AD), exhibit substantial clinical and molecular heterogeneity, complica...
Characterizing two-dimensional quantum materials from optical microscopy images is challenging due to the subtle layer-dependent contrast, limited lab...
Transformer models achieve state-of-the-art performance across domains and tasks, yet their deeply layered representations make their predictions diff...
Background: Australian health practitioners are regulated under the Health Practitioner Regulation National Law, with serious conduct matters referred...
Large language models (LLMs) are increasingly used for qualitative analysis in substance use research, yet their performance relative to human coders ...
Understanding how gene function emerges across molecular, cellular, and pharmacologic contexts remains a central challenge in systems biology and drug...
We introduce a novel uncertainty-aware multimodal segmentation framework that leverages both radiological images and associated clinical text for prec...
The opioid epidemic continues to ravage communities worldwide, straining healthcare systems, disrupting families, and demanding urgent computational s...
Neurological health score (NHS), indicating the health of brain and nervous system, helps in identifying high risk individuals, and in recommending li...
Back pain is a pervasive issue affecting a significant portion of the population, often worsened by certain movements of the lower back. Assessing the...
Recent advances in 3D Large Multimodal Models (LMMs) built on Large Language Models (LLMs) have established the alignment of 3D visual features with L...
Existing multimodal retrieval systems excel at semantic matching but implicitly assume that query-image relevance can be measured in isolation. This p...
This study centers around the design and implementation of the Maya Robot, a portable elephant-shaped social robot, intended to engage with children u...