Using routine clinical data, the model gauges liver cancer risk better than existing tools, offering a potential way to identify high-risk patients missed by current screening criteria.
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A machine learning model may enable liver cancer risk prediction with routine clinical information
A machine learning model that analyzes patient demographics, electronic health record data, and routine blood test results ...
Artificial intelligence (AI) could help physicians determine if survivors of childhood cancer need extra support - and the more information included in AI prompting, the better its performance. This ...
A machine learning model that analyzes patient demographics, electronic health record data, and routine blood test results predicted a patient's risk of hepatocellular carcinoma (HCC), the most common ...
Lung cancer remains the leading cause of cancer-related deaths worldwide, accounting for nearly one in five cancer ...
Skolnick has developed AI-based approaches to predict protein structure and function that may help with drug discovery and ...
Researchers assessed the feasibility of using large language models to match cancer patients with certain genetic mutations to appropriate clinical trials.
A new tool makes it possible to screen millions of tiny protein fragments and select those that can be recognized by the ...
The integration of bioinformatics and medical imaging, often referred to as radiogenomics, has emerged as a powerful and transformative approach in cancer ...
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