Leprosy is a chronic, infectious granulomatous disease caused by Mycobacterium leprae or Mycobacterium lepromatosis, primarily transmitted through multibacillary leprosy patients, with an incubation period ranging from months to 20 years. Although the use of multi⁃drug therapy has significantly reduced the number of leprosy cases, the disease remains a major public health issue in tropical regions, with many individuals still affected or at risk of infection. Early⁃stage leprosy lacks specific symptoms, and the absence of a gold⁃standard diagnostic method poses significant challenges for rapid identification. Many patients are diagnosed only after developing irreversible deformities and disabilities, which inadvertently contributes to ongoing transmission. Early detection of leprosy is crucial yet highly challenging. Emerging artificial intelligence technologies, with their strengths in image recognition, data integration, and predictive analysis, hold immense potential and broad prospects for leprosy prevention and control. This paper outlines conventional diagnostic methods for leprosy and their limitations, while also summarizing research progresses and challenges in artificial intelligence⁃driven leprosy diagnosis. Although obstacles remain, such as algorithm optimization, dataset expansion, and ethical concerns, the continuous evolution and innovation of artificial intelligence technologies promise a worth expecting future for its application in leprosy diagnosis.

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