Artificial intelligence is emerging as a promising support tool for identifying respiratory diseases earlier, with experts highlighting its potential role in tuberculosis (TB) screening and lung cancer assessment at NAPCON 2026. Discussions at the national respiratory medicine conference focused on how AI-assisted medical imaging could help clinicians identify suspicious findings, prioritise patients for further evaluation and improve the efficiency of diagnostic pathways.

The 28th Joint Annual National Conference of the National College of Chest Physicians, India (NCCP-I), and the Indian Chest Society (ICS) was held from October 22 to 25. The conference brought together respiratory physicians, researchers and other healthcare professionals to discuss developments in pulmonary medicine and the future of respiratory disease management.

AI-Assisted TB Screening Could Strengthen Early Identification

Tuberculosis remains a major public health challenge in India, making timely identification and access to diagnostic services important priorities. AI-assisted chest X-ray screening could help healthcare teams identify individuals whose imaging results require further assessment, particularly in locations where specialist expertise is limited.

AI-enabled software can analyse chest radiographs and flag patterns associated with possible tuberculosis. By supporting the initial screening process, these tools may help healthcare workers identify patients who need confirmatory testing and referral.

This approach could be particularly useful in community screening programmes, primary healthcare settings, mobile diagnostic units and underserved regions. In these environments, technology may help clinicians manage large numbers of screening images and direct attention towards people who require additional investigation.

However, an AI-generated alert does not establish that a person has tuberculosis. Clinical assessment and appropriate diagnostic tests remain necessary to determine whether the disease is present.

How Artificial Intelligence Could Support Lung Cancer Detection

Lung cancer is another area in which AI-assisted imaging is attracting interest. Identifying suspicious abnormalities before the disease progresses can be important for timely clinical evaluation and treatment planning.

AI tools can analyse CT scans to highlight pulmonary nodules and other findings that may warrant closer examination. These systems can help radiologists and clinicians identify areas of interest, review complex images and prioritise patients who may need further investigation.

At NAPCON 2026, experts discussed the potential for such technologies to strengthen respiratory disease assessment. Their application could be valuable in healthcare systems that face increasing imaging workloads and shortages of specialised diagnostic resources.

Nevertheless, detecting an abnormality on a scan is not the same as diagnosing cancer. Suspicious findings require interpretation by trained healthcare professionals, with additional investigations guided by the patient’s clinical history and established medical protocols.

Why AI-Based Medical Imaging Matters for India

The potential value of AI in TB and lung cancer detection extends beyond image analysis. When incorporated into an effective diagnostic pathway, these tools may help healthcare providers organise screening services, identify patients requiring attention and reduce avoidable delays between initial assessment and further investigation.

India’s substantial tuberculosis burden makes accessible screening and timely diagnosis particularly important. AI-assisted technologies could support screening initiatives in areas where access to experienced radiologists or respiratory specialists is constrained.

For lung cancer, AI-based image analysis may offer additional support when reviewing CT scans and identifying suspicious nodules. The extent of its benefit, however, depends on the quality of the technology, the population in which it is used and the availability of appropriate follow-up services.

Technology alone cannot resolve gaps in healthcare access. Screening programmes must also have trained staff, functioning diagnostic facilities, reliable referral systems and access to treatment.

Clinical Validation and Responsible Use Remain Essential

Despite the opportunities, the integration of artificial intelligence into respiratory medicine requires careful evaluation. AI systems may perform differently across patient populations, imaging equipment and clinical settings. Their accuracy and reliability therefore need to be assessed in the environments where they will be deployed.

Healthcare institutions must also consider the risks of false-positive and false-negative results. An incorrect alert may lead to unnecessary investigations, while a missed abnormality could create false reassurance and delay further assessment.

Experts have emphasised that AI-assisted imaging should complement, rather than replace, clinical judgement. Doctors must interpret findings in the context of symptoms, medical history, examination results and other relevant investigations.

Data privacy, appropriate staff training, dependable infrastructure and clear clinical responsibility are also important considerations. Hospitals and screening programmes need defined procedures for responding to AI-generated alerts and ensuring that patients receive the necessary follow-up care.

Building a Connected Screening and Diagnostic System

For AI-assisted screening to deliver meaningful results, healthcare providers need to connect initial detection with confirmatory diagnosis and treatment. Identifying a potentially suspicious finding is only the first step in the patient’s care pathway.

A practical implementation model would combine AI-supported image review with qualified clinical interpretation, timely diagnostic testing and appropriate referral. Monitoring real-world performance could also help institutions understand whether these tools improve screening efficiency and patient outcomes.

Collaboration among clinicians, technology developers, healthcare administrators and public health authorities will be important in determining where AI can provide the greatest value. Affordability and equitable access will remain central to ensuring that the benefits are not limited to well-resourced hospitals.

NAPCON 2026 Highlights the Evolving Role of AI in Pulmonary Medicine

The discussions at NAPCON 2026 reflect growing interest in using artificial intelligence to support respiratory healthcare. From tuberculosis screening to lung cancer imaging, AI-based systems may offer additional ways to identify suspicious findings and help clinicians prioritise further evaluation.

The next stage will depend on robust clinical validation, responsible implementation and integration with established diagnostic services. If these requirements are met, AI could become a useful component of broader efforts to strengthen respiratory disease screening and improve access to timely medical assessment in India.

The central objective remains clear: technology should help healthcare professionals make informed decisions and connect patients with appropriate care as early as possible.