Latest AI and machine learning research in cultural competence for healthcare professionals.
Current AIGC detectors often achieve near-perfect accuracy on images produced by the same generator used for training but struggle to generalize to outputs from unseen generators. We trace this failure in part to latent prior bias: detectors learn shortcuts tied to patterns stemming from the initial noise vector rather than learning robust generative artifacts. To address this, we propose On-Man...
As dynamic interfaces governing molecular recognition and signal transduction, interactions between plants and microbes fundamentally shape ecosystem dynamics and evolutionary trajectories. This review summarizes peptides involved in plant-microbe interactions, emphasizing their diversity, biological functions mediated at the cell surface, pharmacological applications, and recent methodological ad...
Speech Emotion Recognition (SER) is a rapidly evolving field of research that aims to identify and categorize emotional states through speech signal a...
Understanding the distribution of plant species diversity(PSD) along spatial and environmental gradients is essential for implementing effective conse...
Artificial intelligence (AI)-based clinical decision support systems (CDSS) hold great promise for mental health (MH) care, offering opportunities to ...
Artificial intelligence (AI) medical scribes (AI scribes), which ambiently record and transcribe patient-clinician interactions into structured docume...
The integration of chatbots into psychiatry introduces a novel approach to support clinical decision-making, but biases in their recommendations pose ...
OBJECTIVES: This work evaluated algorithmic bias in biomarkers classification using electronic pathology reports from female breast cancer cases. Bias...
Visual Grounding is a task that aims to localize a target region in an image based on a free-form natural language description. With the rise of Tra...
Plant DNA methylation changes occur hundreds to thousands of times faster than DNA mutations and can be transmitted transgenerationally, making them u...
Chain-of-thought (CoT) reasoning enhances performance of large language models, but questions remain about whether these reasoning traces faithfully...
The present study performs a comprehensive fairness analysis of machine learning (ML) models for the diagnosis of Mild Cognitive Impairment (MCI) an...
While pre-trained multimodal representations (e.g., CLIP) have shown impressive capabilities, they exhibit significant compositional vulnerabilities...
While bias in large language models (LLMs) is well-studied, similar concerns in vision-language models (VLMs) have received comparatively less atten...
Modern vision-language models (VLMs) often fail at cultural competency evaluations and benchmarks. Given the diversity of applications built upon VL...
Recent advances in diffusion models have led to impressive image generation capabilities, but aligning these models with human preferences remains c...
Text-to-Image (T2I) diffusion models have made remarkable advancements in generative modeling; however, they face a trade-off between inference spee...
As generative AI systems become widely adopted, they enable unprecedented creation levels of synthetic data across text, images, audio, and video mo...
Despite advances in reasoning and planning of R1-like models, Large Language Models (LLMs) still struggle with tasks requiring precise computation, ...
Large Vision Language Models (LVLMs) have achieved remarkable progress in multimodal tasks, yet they also exhibit notable social biases. These biase...