
AI-based analysis identifies the most impactful policy and resource factors for improving cancer survival across 185 countries.
Key Details
- 1Researchers used machine learning to analyze cancer incidence and mortality data from 185 countries.
- 2Study incorporated metrics on radiotherapy access, health system structure, and economic indicators.
- 3Radiotherapy access, universal health coverage, and national wealth repeatedly emerged as top factors for improved survival.
- 4The SHAP-based model provides country-specific roadmaps for policy investment priorities.
- 5An interactive tool allows users to view and compare readiness or gaps for their own country.
- 6The study is published in Annals of Oncology (Jan 2026).
Why It Matters

Source
EurekAlert
Related News

NYU AI Model Improves 5-Year Breast Cancer Risk Prediction via 3D Mammograms
NYU researchers developed an AI model using longitudinal 3D mammograms that outperforms single-scan and 2D-based tools in predicting 5-year breast cancer risk.

AI Framework Accelerates Aortic Aneurysm Risk Prediction from Imaging
Researchers developed BioPINN-LM, combining physics-informed neural networks and multimodal large language models to deliver fast, interpretable risk assessments for ascending thoracic aortic aneurysms.

Study Finds Patient Voices Missing in Generative AI Design for Oncology
A Flinders University-led review found patients and carers are rarely involved in shaping generative AI tools used in oncology.