Latest AI and machine learning research in cultural competence for healthcare professionals.
The emergence of unified multimodal understanding and generation models is rapidly attracting attention because of their ability to enhance instruction-following capabilities while minimizing model redundancy. However, there is a lack of a unified evaluation framework for these models, which would enable an elegant, simplified, and overall evaluation. Current models conduct evaluations on multip...
Effective communication about breast and cervical cancers remains a persistent health challenge, with significant gaps in public understanding of cancer prevention, screening, and treatment, potentially leading to delayed diagnoses and inadequate treatments. This study evaluates the capabilities and limitations of Large Language Models (LLMs) in generating accurate, safe, and accessible cancer-r...
Recent advances in image-based saliency prediction are approaching gold standard performance levels on existing benchmarks. Despite this success, we...
This paper explores the role of Artificial Intelligence (AI) in Public Health (PH), examining its benefits, challenges, and ethical considerations. AI...
Machine Learning models, more specifically Artificial Neural Networks, are transforming medical imaging by enabling precise liver segmentation, a cruc...
Over the past decades, computer-aided diagnosis tools for breast cancer have been developed to enhance screening procedures, yet their clinical adop...
Ensuring fairness is critical when applying artificial intelligence to high-stakes domains such as healthcare, where predictive models trained on im...
Pregnancy-associated dermatologic conditions emerge from intricate hormonal, immunologic, genetic, and environmental changes, often complicating mater...
Ethical dilemmas exist with decision-making regarding resource allocations, such as critical care, ventilators and other critical equipment, and pharm...
Surgical scene segmentation is critical in computer-assisted surgery and is vital for enhancing surgical quality and patient outcomes. Recently, ref...
Chemical synthesis planning has considerably benefited from advances in the field of machine learning. Neural networks can reliably and accurately pre...
This article examines the expanding role of Artificial Intelligence (AI) in healthcare and associated human rights concerns, including whether new EU ...
Segmentation of the airway tree plays a vital role in clinical practice. However, the complex airway tree structure makes it quite challenging to anno...
It has been proven that the microbiome in human bodies can promote or inhibit the treatment effects of the drugs by affecting their toxicities and act...
This paper discusses ethics-based strategies for mitigating bias in machine learning models used to predict sepsis onset. The first part discusses how...
Achieving group-robust generalization in the presence of spurious correlations remains a significant challenge, particularly when bias annotations a...
This systematic review aims to assess the effectiveness of AI-Driven Decision Support Systems in improving glycemic control, measured by Time in Range...
To systematically review the progress in the method development and application of distributed learning in the estimation of epidemiological effect a...
Global patterns of intraspecific genetic diversity are key to understanding evolutionary and ecological processes. However, insights into the distribu...
Although machine learning is frequently used in medicine for predictive purposes, its accuracy in diabetes-related amputation (DRA) remains unclear. F...