Artificial intelligence (AI) is emerging as a powerful tool in the fight against non‑small cell lung cancer (NSCLC), particularly in improving how the disease is diagnosed and understood. Experts say AI can streamline and enhance complex biomarker testing by rapidly analyzing vast amounts of genomic or imaging data, helping to detect actionable mutations that might otherwise be missed. This could help clinicians tailor treatments more precisely to an individual patient’s tumor profile, speeding up decision‑making and improving early‑stage care.
Despite these promising applications, significant challenges remain in NSCLC management that AI aims to address. Screening uptake is still limited, and obtaining enough tissue for comprehensive testing can be difficult, meaning opportunities for early detection are sometimes missed. AI models trained on imaging or pathology data could help identify high‑risk patients sooner and reduce delays in diagnosis, potentially saving lives through earlier intervention.
Beyond diagnostics, AI is also being explored for its potential to assist in treatment planning and response prediction. Advanced machine learning models can help interpret subtle features in CT scans or other imaging modalities that correlate with how a patient may respond to therapies like immunotherapy. There is growing evidence that such AI‑based models can predict treatment efficacy and long‑term outcomes, offering clinicians additional insight when developing personalized treatment strategies.
Experts stress that, while AI holds great promise in NSCLC care, it is not expected to replace oncologists but rather to augment their expertise. Successful integration into clinical workflows will require rigorous validation, clinician oversight, and high‑quality data to ensure reliability and benefit for patients. With ongoing research and careful implementation, AI could help fill critical gaps in lung cancer care — from earlier detection to more personalized therapy and better prediction of disease progression.
