AI Reveals How Each Country Can Boost Cancer Survival

Researchers have harnessed artificial intelligence for the first time to uncover what truly drives cancer survival across nearly every country on Earth. By analyzing global cancer data alongside health system indicators like wealth, insurance coverage, and access to radiotherapy, the team built a machine-learning model that shows which policy changes could save the most lives in each nation. Credit: Shutterstock Global Feature Importance for Mortality-to-Incidence Ratio Prediction. Credit: Dr. Edward Christopher Dee and Milit Patel AI has revealed why cancer survival differs so dramatically around the world, highlighting the specific health system factors that matter most in each country. Researchers have, for the first time, applied machine learning, a form of artificial intelligence (AI), to pinpoint the factors most closely linked to cancer survival across nearly every country worldwide. The findings appear today (January 13) in the leading cancer journal Annals of Oncology . The research goes beyond broad global trends by showing which specific policy actions or system improvements could most effectively boost cancer survival in individual countries. Through an online tool developed by the team, users can select a country and explore which elements, including national income, access to radiotherapy, and universal health coverage, are most strongly connected to cancer outcomes. Turning Data Into Actionable Policy Guidance Dr. Edward Christopher Dee, a resident physician in radiation oncology at Memorial Sloan Kettering (MSK) Cancer Center in New York, USA, and a co-leader of the study, emphasized the wide differences in cancer outcomes across nations. “Global cancer outcomes vary greatly, largely due to differences in national health systems. We wanted to create an actionable, data-driven framework that helps countries identify their most impactful policy levers to reduce cancer mortality and close equity gaps.” He added that several factors repeatedly stood out. “We found that access to radiotherapy, universal health coverage and economic strength were often important levers being associated with better national cancer outcomes. However, other key factors were relevant as well.” Analyzing Cancer Data From 185 Countries To build the model, Dr. Dee and his colleagues analyzed cancer incidence and death data from the Global Cancer Observatory (GLOBOCAN 2022), covering 185 countries. They combined this with health system data from the World Health Organization, the World Bank, United Nations agencies, and the Directory of Radiotherapy Centres. The information included health spending as a share of GDP, GDP per capita, the number of physicians, nurses, midwives, and surgical staff per 1000 people, levels of universal health coverage, access to pathology services, human development indicators, radiotherapy center availability per 1000 people, a gender inequality index, and the share of healthcare costs paid out of pocket. Building the Machine Learning Model The machine learning model was developed by Mr, Milit Patel, the study’s first author. He is a researcher in biochemistry, statistics and data science, healthcare reform and innovation at the University of Texas at Austin, USA, and at MSK. Mr, Patel explained why this approach was chosen. “We chose to use machine learning models because they allow us to generate estimates...

Preview: ~500 words