OBJECTIVE: Understanding and quantifying biases when designing and implementing actionable approaches to increase fairness and inclusion is critical for artificial intelligence (AI) in biomedical applications.
Artificial intelligence (AI) has made significant advances in radiology. Nonetheless, challenges in AI development, validation, and reproducibility persist, primarily due to the lack of high-quality, large-scale, standardized data across the world. A...
The rapid evolution of generative artificial intelligence (AI) models including OpenAI's ChatGPT signals a promising era for medical research. In this Viewpoint, we explore the integration and challenges of large language models (LLMs) in digital pat...
With significant advancements of next generation sequencing technologies, large amounts of multi-omics data, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics, have been accumulated, offering an unprecedented opportunity ...
BACKGROUND: Pediatric stroke is an important cause of morbidity in children. Although research can be challenging, large amounts of data have been captured through collaborative efforts in the International Pediatric Stroke Study (IPSS). This study e...
Proceedings of the National Academy of Sciences of the United States of America
Jul 3, 2024
Efficient storage and sharing of massive biomedical data would open up their wide accessibility to different institutions and disciplines. However, compressors tailored for natural photos/videos are rapidly limited for biomedical data, while emerging...
Artificial intelligence (AI) refers to computer systems doing tasks that usually need human intelligence. AI is constantly changing and is revolutionizing the healthcare field, including nutrition. This review's purpose is four-fold: (i) to investiga...
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