Researchers used artificial intelligence to analyze cancer incidence and death data from the Global Cancer Observatory (GLOBOCAN 2022) for 185 countries.[2][5] A machine learning model calculated the cancer death-to-incidence ratio for each country and identified factors influencing survival.[1][3] Globally, the most significant factors according to SHAP values were GDP per capita (22.5%), number of radiotherapy centers per population (15.4%) and universal health coverage index (12.9%).[1] The model provides country-specific recommendations to improve outcomes, such as expanding radiotherapy and health coverage.[1][2] The results were published in the journal Annals of Oncology and available through the online Global Health Predictor Insights Tool.[2][3] This approach offers evidence-based blueprints for policies to reduce disparities in cancer survival.[1][3]