PURPOSE: To organize the existing literature regarding applications of artificial intelligence (AI) in biopsies and imaging modalities of patients with systemic autoimmune rheumatic diseases (SARDs) and to familiarize readers with the most commonly o...
Self-supervised learning (SSL) has gained significant attention in medical imaging for its ability to leverage large amounts of unlabeled data for effective model pretraining. Among SSL methods, the masked autoencoder (MAE) has proven robust in learn...
BACKGROUND: Generative artificial intelligence (AI) tools like ChatGPT (OpenAI) have garnered significant attention for their potential in fields such as medical education; however, their performance of large language and vision models on medical tes...
This work aims to explore the transferability of the Model for Assessing the value of Artificial Intelligence in medical imaging (MAS-AI) in the Italian context through a case-study.We applied the MAS-AI, a model for assessing AI in healthcare, to fu...
Medicine is one of the most sensitive fields in which artificial intelligence (AI) is extensively used, spanning from medical image analysis to clinical support. Specifically, in medicine, where every decision may severely affect human lives, the iss...
International journal of pharmaceutics
Aug 9, 2025
Nanotheranostics has garnered significant interest for its capacity to improve customized healthcare via targeted and efficient treatment alternatives. Nanotheranostics promises an innovative approach to precision medicine by integrating therapeutic ...
INTRODUCTION: Large Language Models (LLMs) represent a transformative leap in artificial intelligence with the potential to revolutionize radiologic decision-making. This study uniquely evaluates the performance of various LLMs from different vendors...
BACKGROUND: Artificial intelligence (AI) is rapidly advancing in health care, particularly in medical imaging, offering potential for improved efficiency and reduced workload. However, there is little systematic evidence on process factors for succes...
Cardiovascular illnesses continue to be a predominant cause of mortality globally, underscoring the necessity for prompt and precise diagnosis to mitigate consequences and healthcare expenditures. This work presents a complete hybrid methodology that...
Semantic segmentation of medical images is pivotal in applications like disease diagnosis and treatment planning. While deep learning automates this task effectively, it struggles in ultra low-data regimes for the scarcity of annotated segmentation m...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.