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
NIH-Backed AI Model Predicts Cancer Survival Using Single-Cell Data
Researchers have developed scSurvival, a machine learning tool that uses single-cell tumor data to accurately predict cancer patient survival and identify high-risk cell populations.
•EurekAlert
Deep Learning Pathomics Platform Improves Immunotherapy Prediction in Lung Cancer
A deep learning pathomics platform accurately predicts immunotherapy response in metastatic NSCLC using routine pathology slides.
•EurekAlert
AI Pathology Model Outperforms PD-L1 in Predicting NSCLC Immunotherapy Response
MD Anderson's Path-IO machine learning platform accurately predicts immunotherapy responses in metastatic non-small cell lung cancer, surpassing current biomarker standards.