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
MIT Introduces Interactive AI System for Fast Medical Image Annotation
MIT researchers have developed MultiverSeg, an interactive AI tool enabling efficient, user-driven segmentation of biomedical image datasets without prior model training.
•EurekAlert
Study Finds Gaps in FDA Safety Reporting for AI Medical Devices
A study highlights insufficient standardized safety and efficacy assessments for FDA-cleared AI/ML medical devices.
•EurekAlert
UCLA Unveils Light-Based AI System for Energy-Efficient Image Generation
Researchers at UCLA have developed an optical generative AI model that creates images using minimal energy and computational steps.