Opinion: Artificial intelligence may close the gap in lung cancer control
While lung cancer screening is effective in preventing disease and reducing mortality, racial and socioeconomic disparities in screening access have led to worse outcomes for marginalized groups. Now, experts suggest that artificial intelligence (AI) tools may help close that gap.
In a June 20 commentary in Cancer Innovation, pulmonologist Stephen Kuperberg, MPH ’24, and David Christiani, Elkan Blout Professor of Environmental Genetics at Harvard T.H. Chan School of Public Health, wrote that lung cancer screening rates are lower among high-risk patients from Black and Latinx neighborhoods, increasing the risk of late diagnosis and higher mortality.
“The underlying reasons for poor uptake within this population are complex, including structural racism and social and cultural factors … underscoring the vital need for further study of improved methods for optimal data collection,” they wrote.
According to Kuperberg and Christiani, screening can be improved with AI methods such as analyzing large datasets of medical records and predicting disease based on demographic and social factors like patient location, income, and insurance status. “The information [can be] mobilized to determine associations and thus inform policy and potentially reduce socioeconomic barriers to lung cancer survival,” they wrote.
They concluded, “AI technologies will transform reporting, collecting, and processing population data, whether in public datasets and repositories or within institutions, paving the way for discovery and methodology development in lung cancer detection.” They called for continued work on developing AI tools with the capacity to detect malignant lesions at an early stage and reduce mortality.
Read the commentary: Artificial Intelligence-Based Methods: The Path Forward in Achieving Equity in Lung Cancer Screening and Evaluation
