WHEN ALGORITHMS LOOK INTO THE LUNGS: AI EMERGES AS A POTENTIAL ALLY IN DETECTING TUBERCULOSIS AND LUNG CANCER

October 25, 2026

NEW DELHI, October 25, 2026 — Artificial intelligence is opening new possibilities in the early identification of serious lung diseases, with medical experts highlighting its ability to flag signs of tuberculosis on chest X-rays and suspicious lung nodules on computed tomography (CT) scans. The technology could help clinicians identify abnormalities more efficiently, particularly in settings where large numbers of patients must be screened and specialist radiology services are limited.

The potential applications are significant in India, where tuberculosis remains a major public-health challenge and lung cancer requires timely diagnosis. By analysing medical images and drawing attention to patterns that may otherwise require closer examination, AI-based tools can support healthcare professionals in deciding which scans need further assessment.

Experts have pointed to the ability of AI systems to identify imaging features associated with pulmonary tuberculosis on chest radiographs. Such systems can analyse patterns in lung images and flag abnormalities that warrant clinical attention. This capability may be particularly useful in screening programmes, where healthcare workers must assess large volumes of X-rays and identify individuals who need further investigation.

However, an abnormality flagged by an algorithm does not establish that a patient has tuberculosis. The findings must be interpreted alongside symptoms, medical history and appropriate diagnostic tests. Depending on the clinical circumstances, further evaluation may include sputum testing and molecular tests to establish whether the disease is present.

AI is also being used to examine CT scans for pulmonary nodules, some of which may be associated with lung cancer. These nodules can vary in size, shape and appearance, and determining their significance can be challenging. An AI system can help draw attention to suspicious features, measure nodules or assist with comparing images, allowing radiologists to focus on areas requiring closer scrutiny.

The identification of a lung nodule, however, does not automatically mean that a patient has cancer. Many nodules are benign, and their significance depends on factors such as their appearance, size, growth over time and the patient's clinical risk profile. A radiologist and treating clinician must determine whether follow-up imaging, additional investigations or a biopsy is appropriate.

The emerging role of AI is therefore best understood as decision support rather than a replacement for medical expertise. The technology can help organise information and flag potential abnormalities, but diagnosis requires clinical interpretation and, where indicated, confirmatory testing.

One potential advantage is the ability to improve consistency in image assessment. Different levels of experience, workload pressures and the quality of available equipment can influence how quickly abnormalities are recognised. Properly evaluated AI tools may help standardise certain aspects of image review and reduce the likelihood that suspicious findings are overlooked.

The benefits could be especially relevant in areas with shortages of radiologists and other specialists. In high-volume screening settings, automated image analysis may help prioritise patients whose scans require urgent attention. This could allow limited medical resources to be directed towards individuals who are more likely to need further assessment.

Nevertheless, the performance of these systems depends on the quality of the algorithms, the images they analyse and the populations in which they are deployed. A tool trained using one set of patients or imaging equipment may not perform equally well in every hospital or community. Clinical validation, regular monitoring and appropriate oversight are therefore essential before such systems are relied upon in routine care.

False-positive results can lead to anxiety and unnecessary investigations, while false negatives may create unwarranted reassurance. Healthcare institutions must also consider patient privacy, the secure handling of medical images and the responsibility for decisions influenced by automated recommendations.

Experts' emphasis on AI's potential reflects a wider transformation in diagnostic medicine. Medical imaging increasingly combines the expertise of radiologists with computational systems capable of examining complex patterns. When appropriately designed and supervised, these tools may help healthcare teams manage demanding workloads and identify patients who need timely attention.

For tuberculosis, earlier identification can support quicker clinical assessment and reduce delays in initiating appropriate treatment. For suspected lung cancer, the timely evaluation of suspicious nodules can help determine whether further investigation is necessary. In both cases, the ultimate benefit depends on an effective pathway from detection to diagnosis, treatment and follow-up.

AI alone cannot overcome barriers such as limited access to healthcare, delays in confirmatory testing or difficulties in obtaining treatment. Its contribution will depend on whether hospitals and public-health programmes can connect automated alerts to accessible and reliable medical services.

The message from medical experts is that artificial intelligence could become a valuable additional instrument in the fight against serious lung diseases. By flagging possible signs of tuberculosis on X-rays and suspicious nodules on CT scans, it may help clinicians direct attention where it is most needed. But the final responsibility for interpreting findings and deciding on patient care remains with qualified healthcare professionals.

The promise lies not in allowing machines to diagnose patients independently, but in using technology to support earlier recognition, more focused investigation and better-informed clinical decisions.