A new study harnessed AI to map tuberculosis hotspots in Bangui, Central African Republic, identifying high-risk neighborhoods down to 100-meter zones. The model revealed a misalignment between current TB clinic locations and the most affected areas, highlighting a significant gap in diagnosis and treatment. By integrating population, poverty, and health access data, this tool offers a strategic path for targeting underdiagnosed communities. It represents a major leap in data-driven public health planning for infectious disease control.