INER Adopts AI Software to Enhance Lung Disease Detection

Mexico’s National Institute of Respiratory Diseases (INER) has implemented AI software to support the analysis of medical imaging and strengthen early detection of lung cancer and other respiratory diseases, including tuberculosis, chronic obstructive pulmonary disease, and pneumonia.

The tool is now being used to assist physicians in interpreting chest X-rays and is expected to expand to computed tomography scans in a next phase. According to Jorge Alatorre, Head of Oncology Services, INER, the experience has been positive in both clinical care and specialist training, particularly because the platform provides automated support to non-radiologist physicians.

INER performs about 24,000 chest X-rays each year, reflecting the scale of demand for imaging studies at the institute and the potential role of AI in supporting medical workloads. The system is integrated directly into the institute’s Picture Archiving and Communication System (PACS), allowing physicians to view AI-identified findings as soon as an image is opened, without the need to upload files or use external platforms.

Beyond detecting possible abnormalities such as pulmonary nodules, the software generates an interpretive report in Spanish. This feature supports physicians in classifying lesions by risk level and deciding whether complementary studies, such as CT scans, are required.

Alatorre says the adoption of AI also reinforces lung cancer screening programs based on low-dose CT scans for high-risk populations, including individuals over 50 with a history of smoking. Early identification in these groups is considered a key factor in improving clinical outcomes.

As a teaching hospital, INER trains specialists in pulmonology, thoracic surgery, radiology, and related fields. The integration of AI into routine practice allows residents and fellows to learn how to use these tools as part of clinical decision-making, strengthening their professional training.

Looking ahead, INER plans to develop a second-stage model to extend this technology to health centers and hospitals in Mexico City. The proposal includes a first-to-third-level referral scheme, training primary care physicians and deploying the AI system in facilities equipped with X-ray machines to improve timely detection of lung cancer and other respiratory conditions.

For the institute, the adoption of AI represents a strategic step in innovation and quality of care, with potential impact beyond Mexico and into the broader Latin American region.

“Cancer remains a leading cause of death worldwide, but AI and data science have become powerful allies in the fight,” says Seema Verma, Executive Vice President and General Manager, Oracle Health and Life Sciences. 

The application of AI in cancer care is set to experience significant growth, with a BCC Research  report projecting the market will increase from US$2.2 billion in 2024 to US$6.3 billion by 2029. This growth represents a compound annual growth rate (CAGR) of 23.1% over the five-year period, driven by advancements in technology and the rising need for improved cancer diagnosis, treatment, and drug discovery.

BCC Research highlights several key areas where AI is making inroads, including screening, diagnosis, therapy, and drug discovery. The study evaluated its potential role across North America, the European Union, Asia Pacific, and other regions, providing a comprehensive view of AI's growing role in cancer care across diverse regions and healthcare settings.

“AI is already enabling significant advances in healthcare. It allows us to analyze vast datasets and draw conclusions far more rapidly than humans alone could achieve,” says Rubén Briseño, CTO, CTR Scientific, to MBN.

One of the main drivers of AI adoption in cancer care is the increasing global incidence of cancer. As the number of cancer cases continues to rise, healthcare providers are increasingly turning to AI for solutions that can improve early detection and help develop personalized treatment plans. AI’s ability to process vast datasets quickly and accurately offers the potential for more effective and targeted therapies, which is especially important given the growing cancer burden worldwide.

The World Health Organization (WHO) reported 20 million new cancer cases worldwide in 2022, along with 9.7 million deaths. Data from the American Cancer Society also point to a widening gap between women and men, particularly at younger ages. In 2002, cancer incidence among women under 50 was 50% higher than among men in the same age group; by 2021, that difference had increased to 82%.

Cancer is among the noncommunicable diseases that accounted for about 6 million deaths in the Americas in 2021, according to PAHO data. In Mexico, 799,869 deaths were recorded in 2023, with malignant tumors accounting for 91,562 of these cases, equivalent to 11.4% of total mortality, reports INEGI. Cancer-related deaths were slightly higher among women, who represented 52.4% of the total, compared with 47.6% among men.

Looking at the past decade, cancer mortality in Mexico has shown a fluctuating trend. The death rate reached its highest level in 2020, at 71.7 deaths per 100,000 inhabitants. This peak was followed by a modest decline over the next two years, before the rate rose again in 2023.

​​The WHO estimates that over 40% of cancer cases could be avoided through lifestyle changes and preventive care. Yet, in Mexico, late detection is common. For example, more than 60% of breast cancer cases are diagnosed in advanced stages, significantly reducing survival rates, explains Adrián Alcántara, CEO, Doctoralia, on MBN.

Looking ahead, AI is expected to play an even more significant role in the diagnosis and treatment of cancer. By leveraging advanced technologies, such as AI-driven diagnostic tools, doctors, oncologists, and radiologists will be able to diagnose cancer faster and more accurately. This has the potential to drastically improve patient outcomes and revolutionize cancer care at a global scale.

“Advanced algorithms process tissue images, profile molecular characteristics and, using deep learning, help study cancer cells to identify new biomarkers and therapeutic targets, such as protein degraders, beyond what is already validated by science,” writes Oswaldo Bernal, General Manager, Bristol Myers Squibb Mexico, on MBN.