Cancer Diagnosis

Cancer diagnosis is rapidly evolving, driven by the need for more accurate, efficient, and personalized approaches. Current research focuses heavily on leveraging artificial intelligence, particularly deep learning models like transformers, convolutional neural networks (CNNs), and recurrent neural networks (RNNs), to analyze diverse data types including medical images (e.g., histopathology, CT scans, MRI), genomic data, and clinical notes. These AI-powered tools aim to improve diagnostic accuracy, predict treatment response, and personalize cancer care, ultimately impacting patient outcomes and streamlining clinical workflows. The integration of biomedical knowledge into these models is also a key area of focus, enhancing both performance and interpretability.