Artificial intelligence could help doctors flag signs of tuberculosis and lung cancer sooner and prioritise patients for further investigation, experts at NAPCON 2026 said, stressing that the technology must support clinical judgement and established diagnostic procedures.
The potential uses ranged from screening chest X-rays for patterns suggestive of TB to identifying suspicious lung nodules on CT scans. Specialists said these tools could expand access to screening, particularly where expert interpretation was not readily available.
The discussions took place at the 28th Joint Annual National Conference of the National College of Chest Physicians, India, and the Indian Chest Society, held from October 22 to 25.
More than 2,000 pulmonologists, respiratory physicians, researchers and healthcare professionals from India, the United States, the United Kingdom and other countries participated.
Dr Randeep Guleria, former director of AIIMS, Delhi, who inaugurated the conference, said AI could help doctors identify suspicious findings and make faster decisions.
“Artificial intelligence has the potential to change how we approach the early detection of respiratory diseases. By helping identify suspicious findings on chest X-rays and CT scans, AI can support doctors in making faster, more informed decisions and directing patients towards timely investigations,” he said.
AI-powered software can analyse chest X-rays and flag patterns that may indicate TB, helping healthcare workers identify people who require further testing.
The World Health Organisation has recommended computer-aided detection software as an option for TB screening in appropriate populations.
Experts said India, which accounted for approximately 26 per cent of estimated global TB cases in 2024, could particularly benefit from AI-assisted screening in underserved areas, primary healthcare facilities and mobile diagnostic programmes.
Faster identification and referral could reduce delays in diagnosis and treatment, they said. However, confirmatory testing remained essential, and imaging findings needed to be assessed alongside clinical information and established diagnostic protocols.
Dr Raj Kumar, director of Delhi’s Vallabhbhai Patel Chest Institute, said AI-assisted chest X-ray screening offered an opportunity to widen TB detection where specialist expertise was limited.
He said the technology could make screening more efficient and prioritise patients for further evaluation. Its benefits, however, depended on integration with a reliable system connecting patients to confirmatory tests and timely treatment.
For lung cancer, algorithms could help doctors examine CT scans, identify suspicious nodules and highlight abnormalities requiring investigation, potentially improving opportunities for treatment before the disease advanced.
Dr Mohammed Munavvar, senior interventional pulmonologist at Lancashire Teaching Hospitals in the United Kingdom, said outcomes were closely linked to how early lung cancer was identified.
“AI can help clinicians detect and assess suspicious nodules on CT scans and prioritise patients who may need further investigation,” he said.
Responsible use of these algorithms could strengthen diagnostics and support earlier clinical decisions, he added. Their performance needed validation, and trained healthcare professionals had to interpret their findings.
Experts said AI could help respiratory healthcare teams manage large volumes of medical images and identify patients needing attention sooner.
Its success would depend on affordability, reliable infrastructure, appropriate training, validation in Indian populations and clear pathways to confirmatory diagnosis and treatment. They emphasised that AI should complement doctors’ expertise rather than replace it.
“AI offers new possibilities for earlier detection of TB and lung cancer, and our priority must be to translate these advances into practical, accessible solutions that benefit patients across India,” the conference organisers said.