Who is leading medical AI? A systematic review and scientometric analysis of chest x-ray research

Abstract

In this study, we examined the global landscape of research involving computer vision applied to chest X-rays. While these technologies have the potential to significantly improve healthcare worldwide, their effectiveness depends on being developed and tested using data from diverse populations. We analyzed nearly one thousand scientific articles and found that research leadership and data sources are heavily concentrated in high-income countries, particularly the United States and China.Our findings reveal a concerning gap because people in low-income regions, who often face the highest burden of respiratory diseases, are almost entirely absent from the research process as both authors and data contributors. This imbalance creates a risk that medical artificial intelligence may not perform reliably when used in different parts of the world, which could accidentally worsen existing health inequalities. We argue that the scientific community must prioritize international partnerships that treat researchers from developing nations as equal leaders. By making medical data more diverse and accessible, we can ensure that these powerful diagnostic tools benefit patients everywhere, regardless of their location or economic status.