Latest AI and machine learning research in patient safety / risk management for healthcare professionals.
Although still limited, the integration of artificial intelligence (AI) in health care has rapidly expanded in the past few years, especially in oncology clinics. In this article, AI refers to the development and implementation of computer systems capable of performing tasks that typically require human intelligence, such as language understanding, learning, and reasoning. AI technology is current...
BACKGROUND: The clinical diagnosis of skin lesions involves the analysis of dermoscopic and clinical modalities. Dermoscopic images provide detailed views of surface structures, while clinical images offer complementary macroscopic information. Clinicians frequently use the seven-point checklist as an auxiliary tool for melanoma diagnosis and identifying lesion attributes. Supervised deep learning...
As large language models (LLMs) become increasingly integrated into hiring processes, concerns about fairness have gained prominence. When applying ...
Generative artificial intelligence is developing rapidly, impacting humans' interaction with information and digital media. It is increasingly used ...
Long-form question answering (LFQA) presents unique challenges for large language models, requiring the synthesis of coherent, paragraph-length answ...
Despite the success of recommender systems in alleviating information overload, fairness issues have raised concerns in recent years, potentially le...
The increasing prevalence of mental health disorders globally highlights the urgent need for effective digital screening methods that can be used in...
Recent progress in Multimodal Large Language Models (MLLMs) have significantly enhanced the ability of artificial intelligence systems to understand...
Large Language Models (LLMs) rapidly reshape modern life, advancing fields from healthcare to education and beyond. However, alongside their remarka...
Explainable Artificial Intelligence (XAI) is crucial for enhancing transparency, interpretability and actionability of AI systems, particularly in hea...
The intricate and multifaceted nature of vision language model (VLM) development, adaptation, and application necessitates the establishment of clea...
Existing large language models (LLMs) are advancing rapidly and produce outstanding results in image generation tasks, yet their content safety chec...
AIMS: Artificial intelligence (AI) has the potential to transform cardiac electrophysiology (EP), particularly in arrhythmia detection, procedural opt...
Standardised tests using short answer questions (SAQs) are common in postgraduate education. Large language models (LLMs) simulate conversational la...
Recent advances in text-to-image (T2I) generation have achieved impressive results, yet existing models still struggle with prompts that require ric...
This article describes the disclosure avoidance algorithm that the U.S. Census Bureau used to protect the 2020 Census Supplemental Demographic and H...
Artificial intelligence (AI), after surviving two major AI winters (1974-1980 and 1987-2000), is now growing at an exponential rate. This rapid advanc...
BackgroundAs health education robots may potentially become a significant support force in nursing practice in the future, it is imperative to adhere ...
The rapid advancement of generative AI highlights the importance of text-to-image (T2I) security, particularly with the threat of backdoor poisoning...
Deep learning has been reported to achieve high performances in the detection of skin cancer, yet many challenges regarding the reproducibility of r...