Every August 1, World Lung Cancer Day brings renewed attention to the importance of finding lung cancer earlier. This year, the Know Lung Cancer campaign, “Together through lung cancer,” highlights the importance of connection, information, and support for patients and caregivers.[1]
Alongside that message, healthcare systems face another essential question:
What happens after a pulmonary nodule is found?
Is it measured consistently? Is it compared with prior imaging? Is it classified using a standardized framework? And when follow-up is recommended, is the patient supported through the next step — regardless of where the screening took place?
These are not secondary details. They help determine whether the potential benefit of screening is translated into consistent care for patients.
Lung cancer remains one of the most commonly diagnosed cancers and the leading cause of cancer death worldwide. According to the latest GLOBOCAN estimates, approximately 2.6 million people were diagnosed with lung cancer in 2024, while an estimated 1.9 million people died from the disease.[2]
The evidence supporting low-dose computed tomography, or LDCT, screening in eligible high-risk populations is strong. The U.S. National Lung Screening Trial found a 15% to 20% lower risk of lung cancer death among participants screened with LDCT compared with chest X-ray.[3] The Dutch-Belgian NELSON trial subsequently demonstrated significantly lower lung cancer mortality with volume-based CT screening compared with no screening.[4]
These trials established what LDCT screening can achieve within structured protocols. Translating that benefit into routine clinical practice, however, requires more than performing a scan.
It depends on how reliably the entire screening pathway is delivered — from identifying and engaging eligible individuals to acquiring appropriate images, interpreting findings consistently, communicating results, and ensuring that recommended follow-up takes place.
The clinical value of screening depends not only on the scan itself, but on how effectively each step leads to the next.
An effective lung cancer screening program is a sequence of connected steps:
Risk assessment and participation → LDCT acquisition → Nodule detection and measurement → Risk classification → Structured reporting → Follow-up management → Longitudinal comparison → Clinical decision
Each stage depends on the information generated before it.
Detecting a pulmonary nodule is important, but detection alone does not determine what happens next. The finding must be measured and characterized consistently. When prior imaging is available, changes in size or volume must be evaluated over time. The result then needs to be communicated in a form that supports an appropriate management decision.
Standardized frameworks such as the American College of Radiology’s Lung CT Screening Reporting and Data System, or Lung-RADS®, are designed to support consistent reporting, management recommendations, and outcome monitoring in lung cancer screening.[5]
A follow-up recommendation is also not the same as a completed follow-up. When patients are not successfully connected to the next examination or consultation, an opportunity for earlier intervention may be lost.
This is why lung cancer screening should not be viewed as a single imaging event. It is a longitudinal process in which detection, assessment, reporting, and follow-up must remain connected over time.
The role of artificial intelligence in lung cancer screening is sometimes reduced to a single question:
Can it find more nodules?
Nodule detection is important, but it represents only one part of a larger clinical workflow.
AI can support healthcare professionals by helping to detect and quantify pulmonary nodules, compare findings across examinations, and organize results within standardized reporting processes. In high-volume or multi-site screening environments, these capabilities may help reduce repetitive manual work and support more consistent analysis.
Its role is not to diagnose cancer independently or replace clinical judgment. Final interpretation, management decisions, and patient care remain the responsibility of qualified healthcare professionals.
The broader opportunity is to support the parts of the pathway that become increasingly difficult to maintain as screening expands:
For Coreline Soft, the long-term value of medical AI lies not only in identifying findings. It lies in helping healthcare systems turn those findings into information that can be used consistently and followed over time.
Coreline Soft’s perspective has been shaped by years of involvement in lung cancer screening across different healthcare systems.
The company was selected for South Korea’s National Lung Cancer Screening Pilot Project in 2017 and later introduced AVIEW LCS as a quality-management solution for the country’s national screening program.[6]
Across Europe, AVIEW has also been selected for and applied in major lung cancer screening research and implementation initiatives, including HANSE in Germany, IMPULSION in France, and RISP in Italy.[6]
These initiatives operate within different screening protocols, clinical environments, and healthcare structures. However, they share a common requirement: large volumes of imaging information must be analyzed and managed in a consistent and clinically usable way.
AVIEW has now been applied to more than 3 million real-world clinical cases across over 300 healthcare institutions in 19 countries. Coreline Soft has also accumulated regulatory and clinical experience across 21 countries, obtained 12 U.S. FDA 510(k) clearances, and built its global clinical evidence base through more than 500 AVIEW-related clinical publications and academic presentations.[7]
This experience reinforces an important lesson: an algorithm cannot be evaluated only by what it detects in an individual scan. Its practical value also depends on how consistently it can support clinical workflows across institutions, screening programs, and healthcare systems.
Different healthcare systems require different operating models. Screening criteria, reporting standards, available specialists, technical infrastructure, and regulatory requirements vary by country and institution.
The underlying objective, however, remains the same: to help clinically relevant findings move reliably from detection to the appropriate next step.
A low-dose chest CT acquired for lung cancer screening contains clinically relevant information beyond pulmonary nodules.
The same image may also contain findings related to emphysema and coronary artery calcification. Quantitative analysis of this information can support a more comprehensive assessment of the chest CT that has already been acquired, without requiring additional image acquisition for the analysis itself.
This does not mean that every finding should be treated in the same way or that one examination replaces every dedicated diagnostic procedure. Rather, it reflects a broader question for the future of screening:
How can healthcare professionals make more responsible and clinically useful use of the information already present in a chest CT?
This principle is reflected in AVIEW LCS Plus, which supports the analysis of pulmonary nodules, emphysema, and coronary artery calcification from a single chest CT, according to the intended use and regulatory approval applicable in each market.[8]
As screening programs continue to develop, the focus may therefore expand beyond detecting an individual pulmonary nodule. It may also include how imaging information can support a broader understanding of a person’s chest health and contribute to more informed follow-up.
A patient’s opportunity for earlier detection should not be determined by which hospital they visit, how many subspecialty radiologists are available, or which region they live in.
Yet healthcare institutions do not operate with equal resources. Imaging volumes, access to specialists, reporting systems, and follow-up capacity can vary significantly across sites.
Technology alone cannot eliminate these differences. But when it is integrated responsibly into clinical workflows, AI can help healthcare professionals manage screening information more consistently and support programs as they expand across institutions and regions.
On World Lung Cancer Day, raising awareness remains essential. Awareness can encourage people to understand their risk, discuss screening with healthcare professionals, and seek appropriate support.
But the promise of early detection must also be supported by systems that work after the first scan — systems in which findings are measured consistently, results are clearly communicated, and patients are connected to the next appropriate step.
At Coreline Soft, that is the challenge AVIEW has been developed to support: not simply detecting more findings, but helping more clinically relevant findings move through a connected screening pathway.
Because early detection matters most when it leads to the right next step.
Product availability, functions, and intended uses may vary by country and regulatory approval. AVIEW provides information to support qualified healthcare professionals. Final interpretation and clinical decisions remain the responsibility of the healthcare professional.
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