Latest AI and machine learning research in pain management for healthcare professionals.
Objective: To evaluate whether multi-agent LLM architectures with explicit safety verification maintain guideline compliance when their clinical knowledge bases undergo temporal or institutional distribution shift. Materials and Methods: We designed a controlled evaluation framework using 50,000 synthetic type 2 diabetes patients with CKD and hypertension comorbidities (500 per experimental condit...
Multimodal large language models (MLLMs) have advanced visual understanding and reasoning, yet their static parametric knowledge limits their ability to address knowledge-intensive and dynamically evolving open-world problems. To move beyond this limitation, multimodal deep search has emerged as a key direction for open-world information access, evolving from single-turn factual retrieval toward l...
Automatic sleep staging is a critical role in sleep disorder diagnosis, sleep quality assessment, and long-term health monitoring; however, existing a...
Automated pain assessment in real clinics is limited by scarce clinically grounded facial video data with weak labels (often sequence-level self-repor...
Multimodal large language models (MLLMs) increasingly rely on long chain-of-thought reasoning for complex tasks. However, as reasoning sequences lengt...
Background: Analysis of SPES responses often relies on averaging repeated stimulation trials to improve signal quality. However, this may obscure clin...
Multimodal large language models increasingly use sketches, annotations, tools, and intermediate images during reasoning, but it remains unclear wheth...
High-dimensional data with sparse structure and spatio-temporal dependence arise in many scientific domains. We develop a Bayesian feature-extraction ...
Creative AI is moving from single-step asset generation toward long-horizon multimodal production. Although recent generative models can synthesize hi...
Validated measures of pain catastrophizing primarily assess catastrophizing as a stable trait. However, emerging evidence suggests catastrophizing flu...
Objective. To develop an interpretable multimodal machine-learning model for risk stratification of the rapid pain progression phenotype in knee osteo...
Generative artificial intelligence (AI) has emerged as a powerful framework for drug discovery, yet most current approaches follow one-drug-one-gene t...
Pain is a complex and pervasive phenomenon affecting a large percentage of the population, and accurate assessment is essential for effective clinical...
Automatic pain localization, which involves identifying the anatomical origin of pain from peripheral physiological signals without patient self-repor...
Accurate yield forecasting at the individual-plant level is critical for precision agriculture and supply-chain planning, yet public datasets capturin...
Objective: Shunt-dependent hydrocephalus is a common and costly complication of aneurysmal subarachnoid hemorrhage (aSAH), affecting up to 28% of surv...
Introduction: Cerebral amyloid angiopathy (CAA) is characterized by amyloid-beta deposition in cortical and leptomeningeal vessels and associated with...
De novo peptide sequencing detects peptides directly from tandem mass spectra without a protein sequence database, and deep learning has substantially...
Benchmark accuracy in video large language models (LLMs) is often treated as evidence of visual understanding. We audit this assumption across twenty ...
In this paper, we challenge the prevailing view that information dependency (including rote memorization) drives training data exposure to image recon...