AI Cancer Prognoses: How Stanford’s Model Outperforms Doctors
What if a machine could predict cancer outcomes more accurately than your doctor? Meet MUSK, the AI poised to transform cancer care.
Stanford Medicine’s new AI model, MUSK (short for multimodal transformer with unified mask modeling, an interesting name choice tbh), integrates multiple data types—like imaging scans, pathology slides, and medical notes—to predict cancer prognoses with remarkable accuracy.
Unlike traditional models, which analyze one dataset at a time, MUSK evaluates data holistically, mimicking how doctors consider imaging and patient history simultaneously.
Trained on 50 million pathology images and 1 billion text tokens, MUSK achieves a 75% accuracy rate in predicting survival across 16 cancer types, an 11% improvement over doctors.
It also excels in predicting immunotherapy responses and relapse risks, with up to 83% accuracy in melanoma patients. By synthesizing diverse datasets, MUSK offers a powerful tool to guide treatment decisions and improve patient outcomes.
Read the full article on ExtremeTech.
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