Lung cancer kills more people globally than any other cancer, accounting for 1.8 million deaths each year. In India, the situation is particularly grim. The country recorded 81,742 new lung cancer cases in 2022 and 75,031 deaths in the same year. The survival rate sits at approximately 5%, compared to roughly 20% in Western nations. The gap is not primarily a treatment problem. It is a detection problem.
Most patients in India are diagnosed at an advanced stage, when treatment options are limited and outcomes are poor. The country has no national lung cancer screening program. That absence, combined with unique regional risk factors, inadequate diagnostic infrastructure in rural areas, and high rates of tobacco use in both smoked and smokeless forms, has kept survival rates stagnant for years.
Why lung cancer screening in India has lagged
Early screening attempts using sputum analysis and chest X-rays showed limited sensitivity and specificity. Three large randomized trials conducted across major U.S. cancer centers in the 1970s and 1980s failed to demonstrate a mortality benefit from screening smokers, which stalled the development of any public health strategy for early detection.
The introduction of low-dose computed tomography, or LDCT, changed the calculus. Multiple international guidelines now recommend LDCT screening for high-risk individuals, particularly those with a history of 20 or more years of smoking. In the United States, screening has already contributed to a measurable shift in diagnoses from advanced to early-stage cancer.
India has explored LDCT feasibility. A 2019 survey of specialists in northern India found that 80.75% supported integrating LDCT into the national cancer program. At the same time, 67.92% flagged poor finances and logistics as the primary barriers, and nearly half expressed concern about false positives, a legitimate issue in a country where tuberculosis is widespread and can mimic lung lesions on scans. A 2024 systematic review found that LDCT screening in high-tuberculosis-burden countries showed comparable cancer detection rates to lower-burden regions, which offers some reassurance.
How AI is entering the lung cancer screening picture
Artificial intelligence has moved from experimental to applied in radiology. The FDA has approved several AI-based tools for reading chest X-rays and CT scans, and the technology is demonstrating real advantages. AI-driven lung cancer screening has achieved over 90% sensitivity, compared to 70 to 80% with traditional methods, while reducing false positives by up to 30% and boosting specificity to 85 to 90%. Processing time drops from 30 to 60 minutes for a radiologist to a matter of minutes for an AI system.
These tools are particularly relevant for India, where trained radiologists and pathologists are concentrated in urban centers. Peripheral hospitals and rural clinics often lack the human expertise to analyze complex scans accurately. AI can help bridge that gap by automating image analysis and reducing the variability that comes with different levels of reader experience.
The digital health infrastructure that makes it possible
India launched the National Digital Health Mission in August 2020, building an ecosystem that includes the Ayushman Bharat digital health records system, the CoWIN vaccination platform, the Aarogya Setu contact-tracing app, and the e-Sanjeevani telemedicine service. This infrastructure now reaches across the country’s geographic and economic divides.
Researchers have proposed integrating AI-driven screening algorithms into this existing framework. Using a patient’s Ayushman Bharat health record, an algorithm could combine clinical data, including age, sex, tobacco use history, occupational exposure, and infectious disease history, with radiological findings to flag high-risk individuals for further testing at tertiary centers. The Indian Council of Medical Research has also begun geo-mapping pathology services across every state and district, which will help identify where diagnostic resources exist and where gaps remain.
The lung cancer development process takes between 10 and 20 years from the first cellular mutations to invasive disease. That window represents the clearest opportunity for screening to intervene. With AI tools now capable of detecting early nodules and distinguishing benign from malignant lesions with high accuracy, the technology exists. The remaining work is building the systems in India to deploy it at scale.
