Memorial Sloan Kettering researchers report advancements in AI governance, biomarker analysis, and language models for improved cancer care.
Key Details
- 1MSK implemented governance covering 26 AI models, two ambient pilots, and 33 nomograms, demonstrating scalable AI oversight.
- 2AI tool EAGLE analyzed over 8,000 lung cancer slides, reducing molecular testing by over 40% while maintaining standards.
- 3A cancer-trained LLM ('Woollie') was built from 40,000+ radiology reports; achieved predictive scores of 97 (MSK) and 88 (UCSF) overall.
- 4Study on 118 nonagenarians showed lung cancer surgery can be safe and effective, with no patients dying within 90 days.
- 5Drug combination shown to induce mutation (MMRd) in colorectal tumors, potentially sensitizing resistant cancers to immunotherapy, but no clinical responses observed yet.
Why It Matters
Robust governance and real-world validation are crucial for integrating AI into oncology workflows. New computational tools, especially those leveraging radiology and pathology data, show promise for improving clinical decision-making and operational efficiency in precision cancer care.
Source
EurekAlert
Related News
•EurekAlert
UVA's $4M NIH Grant Backs AI to Optimize Cardiac MRI Therapies
UVA secures $4 million NIH funding to develop GenStrain, an AI model using MRI data to personalize cardiac pacing therapies.
•EurekAlert
Machine Learning Models Predict Cancer Risk with Routine Health Data
Researchers developed machine learning models using routinely collected health data to predict individual risk for 20 common cancers, outperforming traditional methods for several cancer types.
•EurekAlert
Duke Receives $25M to Lead National Trustworthy AI Hub for Dementia and Aging
Duke University has launched a $25 million national center to make AI for aging and dementia clinically robust and trustworthy.