Artificial intelligence is increasingly being applied across lung cancer care to support earlier detection, more individualised risk assessment, treatment planning and ongoing monitoring. Lung cancer remains a leading cause of cancer-related death worldwide, with many patients still diagnosed at advanced stages when treatment options are more limited, survival is lower and care is more costly. AI offers a route towards a more proactive, risk-based model by combining clinical information with algorithmic analysis. Its potential spans screening, nodule assessment, pathology, multimodal biomarker analysis and remote monitoring, but wider adoption depends on governance, workflow integration and clinician oversight.
Earlier Detection Could Broaden Screening Reach
Late diagnosis remains central to the clinical and economic burden of lung cancer. Five-year survival is reported at 10–12% for late-stage disease compared with approximately 65–67% when diagnosis occurs at an early stage. Advanced disease also carries substantially higher treatment and resource costs, with annual expenditure on advanced non-small cell lung cancer in Europe exceeding €3 billion across primary care, hospitalisation and long-term treatment needs. Earlier diagnosis therefore changes both the range of available treatment and the burden placed on healthcare resources.
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Lung cancer screening remains limited globally, and existing eligibility criteria can exclude people who are nevertheless at risk. AI-based risk assessment could help identify individuals who may benefit from screening even when they fall outside conventional criteria. These tools can analyse longitudinal information already available in clinical records, including routine laboratory results, medication histories and other variables. Integrated into electronic health records, such approaches may support more dynamic screening-enrichment strategies rather than relying only on static age or smoking-history thresholds.
AI is also being applied to computer-aided diagnosis. Deep-learning models can assist with lung nodule detection and image analysis, helping clinicians distinguish between higher- and lower-risk findings and prioritise people who may need further evaluation. The same tools may reduce the burden created by false-positive findings. By supporting quicker risk assessment and more informed clinical decisions, AI has the potential to shorten the pathway from detection to diagnosis and enable more timely treatment.
Precision Oncology Moves Beyond Traditional Biomarkers
After diagnosis, AI can support a more detailed characterisation of disease by extracting information from pathology, imaging and molecular data. One approach is pathomics, which applies machine learning to digitised tissue sections. Routine biopsy images can be converted into high-dimensional datasets that reveal morphological patterns such as tissue architecture and immune-cell infiltration, extending analysis beyond what is visible through conventional examination. This creates a bridge between visual pathology and molecular information.
In some cases, these patterns may be used to predict gene mutations directly from pathology slides, with the potential to reduce the time and cost associated with traditional molecular testing. AI can also combine pathology, radiomics and transcriptomic information in a single disease model. This multimodal approach may improve prognostic assessment and help predict therapeutic response, including identifying patients more likely to benefit from immunotherapy.
Foundation models offer another route to scaling these applications. Trained on large pathology datasets, they capture broad biological representations that can be adapted to different clinical tasks and may require fewer samples to reach high accuracy. This approach may make advanced analysis more scalable, including for rare cancer subtypes. Monitoring can also extend beyond hospital settings through wearable devices and circulating biomarkers. Continuous streams of patient data may support more proactive interventions and strengthen long-term postoperative surveillance, broadening the role of AI from diagnosis and treatment selection to continuing follow-up.
Governance and Workflow Determine Clinical Adoption
Moving from high-performing algorithms to routine clinical use requires more than technical accuracy. Healthcare systems need governance structures that support transparency, accountability and real-world clinical value. AI is positioned as a decision-support tool within multidisciplinary workflows, augmenting rather than replacing clinical expertise. Automating time-consuming tasks may also ease workforce pressures by allowing specialists to focus more attention on complex clinical reasoning and decision-making. This hybrid model depends on continued human oversight and patient-focused care.
Trust depends partly on whether clinicians can understand how an algorithm reaches its output. Explainable AI techniques such as spatial heatmaps can provide information about the rationale behind predictions. AI outputs should also appear within existing clinical workflows and electronic health records rather than through disconnected systems. International interoperability standards are presented as one route to supporting scalable integration.
Real-world validation remains another requirement. Targeted pilot programmes can measure workflow integration, user trust and baseline cost-effectiveness before wider deployment. Implementation planning also needs to account for personnel training and other operational costs alongside substantial upfront integration costs. Clinical champions and continuing professional development can support adoption by building AI literacy and confidence within care teams. Together, governance, workflow design and change management form the basis for broader clinical deployment.
AI is moving into multiple parts of the lung cancer pathway, from identifying people who may otherwise miss screening to supporting nodule assessment, pathology analysis, treatment selection and follow-up. Its strongest potential lies in helping healthcare systems shift towards earlier detection and more individualised care while making better use of existing clinical data. That potential is not automatic. Sustainable adoption depends on real-world validation, transparent governance, integration into everyday workflows and continued clinician oversight. Used as clinical decision support rather than a substitute for expertise, AI may strengthen a more proactive model of lung cancer care.
Source: Roche
Image Credit: iStock
