This survey explores the transformative impact of foundation models (FMs) in artificial intelligence, focusing on their integration with federated learning (FL) in biomedical research. Foundation models such as ChatGPT, LLaMa, and CLIP, which are trained on vast datasets through methods including unsupervised pretraining, self-supervised learning, instructed fine-tuning, and reinforcement learning from human feedback, represent significant advancements in machine learning. These models, with their ability to generate coherent text and realistic images, are crucial for biomedical applications that require processing diverse data forms such as clinical reports, diagnostic images, and multimodal patient interactions.
View Article and Find Full Text PDFBackground: Primary hypertension (PH) poses significant risks to children and adolescents. Few prediction models for the risk of PH in children and adolescents currently exist, posing a challenge for doctors in making informed clinical decisions.
Objective: This study aimed to investigate the incidence and risk factors of PH in Chinese children and adolescents.
Angew Chem Int Ed Engl
January 2025
Li-TFSI/t-BP is the most widely utilized p-dopant for hole-transporting materials (HTMs) in state-of-the-art perovskite solar cells (PSCs). However, its nonuniformity of doping, along with the hygroscopicity and migration of dopants, results in the devices that exhibit limited stability and performance. This study reports the use of a spherical anion of the p-dopant, regulated by its radius and shape, as an alternative to the linear TFSI- anion.
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