Comprehensive Summary
This article reviews how artificial intelligence is being used to fight tuberculosis, especially in countries where health resources are limited. The authors searched for studies published in the last five years and found 34 that focused on AI and TB. They grouped the research into areas such as case finding, triage, diagnosis of drug-resistant TB, prediction of treatment outcomes, and mapping where TB cases are likely to occur. Across these studies, AI systems showed strong accuracy in identifying TB, separating it from other lung diseases, and even predicting how patients might respond to treatment. AI also proved helpful for spotting hidden clusters of TB faster than traditional public health systems.
Outcomes and Implications
The review shows that AI could play a major role in strengthening TB control worldwide. Since TB is still one of the top infectious killers, especially in low- and middle-income countries, faster and more accurate tools are badly needed. AI could reduce the pressure on overworked health systems by helping doctors read scans, detect drug resistance, and guide treatment. It may also help governments track outbreaks and direct resources where they are most needed. Still, the authors caution that these tools must be tested more widely before they are used in real-world care. If successful, AI could become an important step toward lowering TB deaths and controlling its spread.