Yihang Wang, Lukas Herron, Pratyush Tiwary
From data to noise to data for mixing physics across temperatures with generative artificial intelligence
Proceedings of the National Academy of Sciences of the United States of America
machine learning ml subset artificial intelligence crucial spine care research due ability improve treatment selection outcomes leveraging vast amounts data generated health care accurate diagnoses decision support mls potential spine care particularly notable radiological image analysis including localization labeling anatomical structures detection classification radiological findings prediction clinical outcomes thereby paving way personalized medicine manuscript discusses mls application spine care detailing supervised unsupervised learning regression classification clustering highlights importance internal external validation assessing ml model performance several ml algorithms linear models support vector machines decision trees neural networks deep convolutional neural networks used spine domain analyze diverse data types visual tabular omics multimodal