Latest AI and machine learning research in cultural competence for healthcare professionals.
Machine learning (ML)- and artificial intelligence (AI)-based aging clocks are increasingly used to quantify physiological and molecular aging from omics and medical imaging data as distinct from chronological age. Here, we characterize a fundamental but underappreciated computational limitation of commonly used ML/AI regression models: systematic prediction bias and its propagation to downstream ...
Machine learning frequently encounters challenges when applied to population-stratified datasets, where data representation bias and data distribution shifts substantially impact model performance and generalizability across different population groups. These challenges are well illustrated in the context of polygenic prediction for diverse ancestry groups, and the underlying mechanisms are broadl...
Fixed larval specimens often shrink and curve, making length measurement labor-intensive. Although recent studies have demonstrated efficient fish-len...
Introduction: Large-scale free-text data with socio-demographic information can capture nuanced accounts of lived experience that are difficult to det...
Robust training and validation of Autonomous Driving Systems (ADS) require massive, diverse datasets. Proprietary data collected by Autonomous Vehicle...
Accurate gene prediction remains a major bottleneck in fungal genomics, where lineage diversity and alternative splicing challenge existing ab initio ...
Deep neural networks have achieved impressive performance across a wide range of tasks, but this success often comes with substantial computational an...
While hyperspectral imaging provides rich spatial-spectral information across hundreds of narrow wavelength bands for precise material identification,...
Skin-tone labels are absent from public dermoscopy benchmarks such as the International Skin Imaging Collaboration (ISIC), making it impossible to aud...
Accurately predicting individual aesthetic evaluation for images is a fundamental challenge for AI. Various deep learning (DL)-based models have been ...
Distilled one-step (T=1) or few-step (T$\leq$4) diffusion models enable real-time image generation but often exhibit reduced sample diversity compared...
RLHF is widely used to align flow-matching text-to-image models with human preferences, but often leads to severe diversity collapse after fine-tuning...
Implicit Neural Representations (INRs) model continuous signals using multilayer perceptrons (MLPs), enabling compact, differentiable, and high-fideli...
Predicting cellular responses to genetic or chemical perturbations across biological contexts is central to drug development and disease understanding...
Annotating bounding boxes is costly and limits the scalability of object detection. This challenge is compounded by the need to preserve high accuracy...
Self-verification, re-invoking the same vision language model (VLM) in a fresh context to check its own generated answer, is increasingly used as a de...
Machine learning continues to accelerate peptide and protein design through the rapid prediction and generation of sequences with desired characterist...
Labeled datasets reflect the biases of their annotation pipelines, which sometimes introduce label bias: group-conditional label errors that cause sys...
While Multimodal Large Language Models (MLLMs) are increasingly integrated with Retrieval-Augmented Generation (RAG) to mitigate hallucinations, the i...
In routine care, individuals identified a priori as high-risk are usually tested for conditions more frequently. Protected attributes, such as sex or ...