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A recent review concluded that using artificial intelligence to predict cancer drug resistance holds significant transformative potential for precision oncology, emphasizing the integration of omics data and advanced models such as machine learning and deep learning. The researchers indicate that AI applications require standardized databases and multimodal integration strategies, with a focus on patients at higher risk of cancer-associated thrombosis, to improve predictions and guide treatments more accurately.
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