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New AI Method Enhances Tumor Analysis Speed and Accuracy

Researchers have developed HD-Yolo, a deep learning-based approach that significantly improves the speed and accuracy of nucleus segmentation and tumor microenvironment characterization in pathology slides. This innovative method outperforms existing techniques, promising advancements in cancer diagnosis and research.

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ChatGPT can extract data from clinical notes

ChatGPT, the artificial intelligence (AI) chatbot designed to assist with language-based tasks, can effectively extract data for research purposes from physicians’ clinical notes, UT Southwestern Medical Center researchers report in a new study.

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New Data Commons Encourages Osteosarcoma Research

Siri's latest trick is offering a hands-free TV viewing experience, that will allow consumers to turn on Bringing together clinical, genomic, and imaging data in a user-friendly format, the Osteosarcoma Explorer is designed to accelerate osteosarcoma research and encourage new contributions to the field.

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SAI accurately predicts cancer outcomes from tissue samples

Researchers at UT Southwestern Medical Center have developed a novel artificial intelligence (AI) model that analyzes the spatial arrangement of cells in tissue samples. This innovative approach, detailed in Nature Communications, accurately predicted outcomes for cancer patients, marking a significant advancement in utilizing AI for cancer prognosis and personalized treatment strategies.

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Using artificial intelligence to personalize lung cancer treatment

UTSW research combining artificial intelligence with traditional pathology analysis holds potential for quickly creating a personalized attack plan for cancer patients when speed is essential: as non-small cell lung cancers spread. This approach identified lung cancers that are most likely to respond to one common treatment versus those that might benefit from a different approach.

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