Despite decades of effort to eliminate it, tuberculosis (TB) remains one of the most pressing health challenges in India. However, with the integration of AI into TB diagnosis and treatment, the country is witnessing a shift in its fight against the disease. The role of AI-assisted treatment and diagnosis in achieving a TB-free India was discussed at the recent World TB Day 2025 Summit held in Delhi.
India’s intensified TB elimination strategy is deploying AI-powered handheld X-ray machines. These devices, combined with AI-based interpretation, have significantly improved the detection of asymptomatic TB cases.
During the 100-day TB Mukt Bharat Abhiyan, over 12.97 crore people were screened, detecting 7.19 lakh TB patients, including approximately 2.85 lakh asymptomatic cases. “The campaign strategically screened vulnerable populations, including asymptomatic individuals, household contacts of TB patients, those with a history of TB, undernourished individuals and those with chronic comorbidities such as diabetes and HIV,” Union health minister JP Nadda said.
The campaign has demonstrated how AI can help overcome geographical barriers to TB diagnosis. Mobile diagnostic vans equipped with nucleic acid amplification testing (NAAT) and AI-assisted X-ray machines were deployed to reach underserved communities.
By using AI, the campaign also focused on high-risk groups such as individuals with HIV, diabetes and malnutrition.
The use of AI to analyse microscopy slides and AI-assisted handheld X-rays for the automated detection of tuberculosis has been a significant breakthrough, particularly in remote areas where healthcare professionals are less in number.
Dr Mathew Varghese, a pulmonologist, told AIM, “Using AI could perhaps aid in screening, data collecting, and reducing burnout within the healthcare staff.”
Advancements in screening methods have expanded the range of tests available for detecting TB in samples beyond sputum. Some of these tests identify antibodies in the blood with the help of AI.
In contrast, others use PCR to detect the pathogen in alternative samples or rely on a cost-effective genetic material amplification technique known as loop-mediated isothermal amplification (LAMP). “For many patients, especially children with tuberculosis, producing a sputum sample can be extremely challenging. These alternative testing methods are valuable in diagnosing TB in individuals who are unable to provide sputum,” another doctor said.
Wadhwani AI, an NGO, is helping India activate this mission to eradicate tuberculosis with AI-powered TB detection and more. Their AI-based tools assist in interpreting diagnostic tests, such as Line Probe Assays (LPA), which help identify drug-resistant TB cases. These machine learning models also help health workers make informed decisions by analysing patient data and predicting risks with greater accuracy and speed.
Dr Sharon Baisil, an assistant professor of community medicine, said in conversation with AIM, “For the elimination of TB, there is still a long way to go. We still diagnose at least two cases a day in our hospital. Perhaps AI could aid the mission if the implementation of AI at the grassroots levels happens.”
Post detection, AI-powered mobile applications and chatbots have been introduced to track patients’ medication adherence, provide reminders and offer real-time support. The Ni-kshay Mitra initiative, which connects TB patients with volunteers who provide nutritional and emotional support, has also been integrated with AI-based tracking systems to ensure efficiency in aid distribution.