AI cuts diagnostic delays in lung cancer detection, offering a blueprint for faster, smarter hospital care

A new study presented at the European Society for Medical Oncology (ESMO) AI Congress demonstrates how AI can help identify patients passively at risk of early-stage lung cancer coming to different departments, long before traditional pathways might flag a concern. 

Lung cancer remains the leading cause of cancer deaths In Europe. While proactive low-dose CT screening for lung cancer is established in some countries, it is not globally standard. This research provides compelling evidence that AI-assisted chest X-rays detection could bridge the gap, turning every hospital radiology department into a potential early detection point The main advantage is its passive nature and the fact that the huge volumes of Chest X-rays of the patients coming to different departments can be .passively looked for risk of Lung Cancer and can be flagged.. 

The study analysed over 30,000 chest X-rays taken for a wide range of clinical indications. Using Qure.ai’s AI-powered algorithm for chest x-ray interpretation, 71 individuals were flagged as high-risk for malignancy. Follow-up CT scans and biopsies confirmed five lung cancer cases, ranging from early to advanced stages. One case was detected at a very early stage (Stage IA3), allowing timely treatment of the patient which could have been missed if AI was not used 

“These findings illustrate how AI can act as a silent accelerator within the diagnostic pathway,” said Dr. Rahul Ravind, Medical Oncologist, Renai Medicity, Kochi and lead investigator. “By embedding AI into everyday hospital workflows, clinicians can catch potential cancers much earlier, even when tests were ordered for non-oncological reasons. AI can help extend the benefits of early diagnosis beyond formal screening programs, making it possible even in hospitals that may not have dedicated lung cancer pathways or CT-based screening access.” 

“We’re seeing AI bridge one of healthcare’s biggest gaps – the time between the first test and the right diagnosis. That acceleration can completely change outcomes for patients. This study underscores AI’s potential to transform opportunistic screening using chest X-rays that are already part of routine care. By alerting clinicians to suspicious findings in real time, AI can shorten diagnostic timelines and enable faster referral, improving chances of early treatment,” says Sagar Sen, VP, life sciences, Qure.ai  

The findings are based on real-world data from Renai Medicity, a tertiary care hospital in Kochi, India, collected between March 2024 and July 2025.