Lung cancer remains the leading cause of cancer-related deaths worldwide, yet advances in computed tomography (CT) screening and artificial intelligence are reshaping prospects for earlier detection and improved outcomes. Evidence from large screening programmes and long-term follow-up cohorts indicates that identifying disease at an early stage can transform survival, while steadily improving CT resolution and lower radiation dose have strengthened the case for wider access. At the same time, health systems continue to face practical hurdles, including low specificity, administrative complexity and patient drop-off from repeated follow-up imaging. AI-enabled tools are emerging to address these bottlenecks by refining risk assessment for indeterminate nodules and helping target resources where they are most likely to benefit patients.
From Screening Milestones to AI Adoption
A pivotal turning point for lung cancer screening arrived with results showing a 20% reduction in deaths when high-risk populations were screened using CT. That signal of benefit prompted the to recommend lung cancer screening for high-risk patients and catalysed broader reimbursement for CT-based programmes. At the same time, the first AI system for detecting lung nodules on CT gained regulatory clearance, marking an early intersection between algorithmic support and lung cancer diagnosis.
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Since then, adoption has broadened. More than a dozen nodule detectors are available across the United States and Europe. CT image quality has improved, and radiation dose has decreased, supporting clinical confidence in screening pathways. Long-term outcomes have added further weight: a 20-year follow-up analysis from I-ELCAP reported survival just over 80% when lung cancer was detected early through CT screening. Against a background where late presentation is common and prognosis is poor once symptoms appear, these results underscore the value of shifting detection earlier.
AI has been positioned as a complementary layer in this evolution. With software designed to enhance sensitivity and streamline image interrogation, radiologists can assess scans more quickly and consistently. The premise is not to supplant clinical judgement but to reinforce it with pattern recognition honed on large datasets, bringing consistency to nodule detection and characterisation at scale.
Tackling Specificity and Follow-Up Burden
Despite gains in sensitivity, low specificity remains a major operational challenge. False positives create a cascade effect for health services, as suspected findings trigger surveillance imaging or invasive procedures that may ultimately prove unnecessary. Nodules are identified in about 40% of chest CT scans. Many of these require tracking over time, with follow-up scans scheduled months apart, often more than once, to observe growth patterns suggestive of malignancy. This process is costly and administratively demanding, and it can erode patient engagement given the time commitment and anxiety associated with prolonged surveillance.
AI is being directed at this pressure point. In 2023, an AI tool was reported to differentiate high-risk from low-risk nodules. The impact of more precise triage is illustrated by small nodules, which carry less than a 1% likelihood of malignancy. When such nodules were flagged by AI as high risk, the likelihood rose to almost 20%. While clinical decisions still rest on a comprehensive assessment, this recalibration of risk at the first appointment can reduce unnecessary downstream imaging and invasive workups, helping focus attention and resources on those with a higher probability of disease.
By improving initial accuracy, AI-supported workflows aim to curb repeated scans and streamline decision-making. The objective is to maintain sensitivity for early disease while reducing the rate of unnecessary biopsies and appointments that do not change outcomes. If radiology teams can distinguish benign from suspicious findings sooner, health systems may alleviate operational bottlenecks while sustaining patient trust and adherence to clinically important follow-up when it is genuinely indicated.
Broadening Risk Assessment Beyond Smoking
Identifying who should be screened remains another hurdle. Smoking history is a well-established risk factor, yet it does not capture the entire burden of disease. Approximately 20% of lung cancers diagnosed in the current year are expected to occur in people who have never smoked. Evidence from 2022 indicates that when incidentally detected nodules are tracked, about half of the lung cancers found arise in patients who would not have met current screening eligibility, either because they were non-smokers or had not accumulated sufficient smoking exposure to qualify.
These patterns point to a gap in current risk stratification approaches. AI is well suited to integrate multiple risk factors and large volumes of imaging and clinical data to refine who is considered high risk. By drawing on diverse inputs, algorithmic models can support more personalised decisions on who to scan, when to scan and how intensively to monitor indeterminate findings. The aim is smarter inclusion rather than indiscriminate expansion, with the aim of enabling earlier detection among individuals who might otherwise fall outside traditional criteria yet harbour clinically significant disease.
In parallel, AI-derived risk scores for nodules provide a common language for multidisciplinary teams, aligning radiology, pulmonology and oncology around consistent thresholds for action. The emphasis remains on augmenting clinical pathways, promoting timely diagnosis and minimising unnecessary interventions.
Evidence supporting CT screening for high-risk groups, reinforced by long-term survival data for early-stage disease, has set the stage for AI to strengthen lung cancer detection. The current priorities are clear: improve specificity to reduce false positives and surveillance burden and broaden risk assessment so that eligibility does not miss a substantial share of cases in never-smokers or those outside legacy criteria. AI-enabled tools that refine nodule risk and integrate diverse factors can help radiology teams focus follow-up where it matters most, supporting earlier diagnosis, fewer unnecessary procedures and more efficient use of resources.
Source: HIT Consultant
Image Credit: iStock
