From prevention to early diagnosis, technological advances and artificial intelligence are transforming medicine, providing increasingly sophisticated tools to detect abnormalities and diseases, improve the effectiveness of screening programmes and support healthcare professionals.
This innovation is particularly valuable in the fight against cancer, including lung cancer, where detecting a lesion early can make a significant difference to treatment options and survival.
Lung nodules: why early detection matters
Early diagnosis is one of the most important tools in the fight against cancer. The issue has recently returned to the spotlight following Pope Leo XIV’s recent announcement that he would undergo surgery to remove a lung nodule discovered during routine medical examinations. His announcement highlights the importance of early detection and the ability to identify abnormalities even in the absence of obvious symptoms.
But what exactly is a lung nodule?
A lung nodule is a small, well-defined, usually rounded area within lung tissue that can be detected through diagnostic imaging, such as computed tomography (CT). By definition, a lung nodule measures no more than three centimetres in diameter.
Its presence does not necessarily indicate cancer. Many nodules are benign and may result from infections, inflammation, or scar tissue. Others, however, may be an early sign of cancer.
Detecting a suspicious lesion and accurately assessing its characteristics makes it possible to arrange appropriate follow-up investigations and, when necessary, intervene at a stage when treatment options may be more effective.
The impact of early diagnosis: up to 24% fewer lung cancer deaths
The benefits of early detection are supported by major international scientific studies.
The National Lung Screening Trial, conducted in the United States with more than 53,000 high-risk participants, showed that screening with low-dose CT can reduce lung cancer mortality by approximately 20% compared with conventional chest X-rays.
In practical terms, this translates into approximately three fewer lung cancer deaths per 1,000 people enrolled in the screening programme over a period of around seven years.
Significant findings also emerged from the European NELSON study, which reported a 24% reduction in lung cancer mortality among screened men compared with the control group after ten years of follow-up.
These findings confirm the value of low-dose CT screening for people at high risk, identified on the basis of factors such as age and smoking history.
They also show how profoundly early diagnosis can affect survival. Artificial intelligence now offers further opportunities to improve diagnostic workflows, supporting specialists with increasingly advanced analytical tools.
Artificial Intelligence supporting early diagnosis
Artificial intelligence is transforming diagnostic medicine, giving doctors increasingly advanced tools to detect abnormalities and diseases at an early stage. In lung cancer diagnosis, a single CT scan can produce hundreds of images that radiologists must examine carefully to identify even very small lesions.
Using algorithms capable of rapidly analysing large amounts of data, AI can help specialists identify suspicious nodules, assess their characteristics and monitor how they change over time.
This support could be particularly valuable in screening programmes, where large volumes of examinations demand efficient processes and accurate assessments. Several studies have highlighted the potential of these systems to improve lung nodule detection, although further evaluation is needed to establish their reliability and impact in clinical practice.
The aim is not to replace doctors, but to support them by automating some repetitive tasks and allowing them to devote more attention to complex cases and patient care.
AILANTON: Italian AI supporting early diagnosis
A tangible example of how technological innovation can help advance medicine is AILANTON, an artificial intelligence project developed by Olidata in collaboration with the Fondazione Policlinico Universitario Campus Bio-Medico in Rome, with support from the Lazio Region.
Designed to make lung nodule detection faster and more accurate, AILANTON uses neural networks to automatically analyse CT images, detect and classify abnormalities, and provide radiologists with useful information about lesions, including an assessment of suspected malignancy.
One of the project’s distinguishing features is its ability to integrate AI analysis directly into hospital information systems by generating structured reports that doctors can review, check, and validate.
The technology is therefore designed to fit into the day-to-day workflows of radiology departments, combining the processing capabilities of algorithms with specialists’ clinical expertise. This approach aims not only to improve image analysis but also to make the entire diagnostic pathway more efficient.
From diagnostic accuracy to time savings
The initial results reported during AILANTON’s testing phase point to promising opportunities for the use of artificial intelligence in diagnostics.
In March 2024, Campus Bio-Medico reported accuracy of up to 98% in the reporting of tumour lesions, noting that experimental use of the system could enable doctors to save approximately 20% of their time.
These figures take on practical significance in everyday hospital operations: reducing the time required to analyse images and prepare reports could allow specialists to focus more on complex cases and their relationships with patients, while supporting more efficient management of diagnostic examinations.
These are promising experimental findings that require further clinical evidence to demonstrate a direct impact on survival. Nevertheless, they highlight the potential of a diagnostic approach in which technological innovation and medical expertise work together.
The future of medicine also depends on this: the ability to turn research and innovation into practical tools that serve human health.
Through AILANTON, Olidata contributes to the development of AI solutions for healthcare, bringing together technological expertise and clinical research. The aim is to make diagnostic processes increasingly timely and efficient, putting technology at the service of healthcare professionals and, above all, patients.