Artificial intelligence (AI) is transforming the management of non-small cell lung cancer (NSCLC) by enhancing precision medicine approaches. AI-driven diagnostic tools analyze large imaging data sets with high precision, identifying subtle patterns often missed by human observers. This leads to increased accuracy in detecting NSCLC. Deep learning models can predict genetic mutations, such as EGFR and ALK rearrangements, from imaging data.
For instance, AI models have predicted EGFR mutations and ALK rearrangement status with areas under the curve (AUCs) of 0.897 and 0.995, respectively. AI also provides individualized prognostic data based on personalized patient information, such as radiographic features of a patient's specific CT scan. Deep learning models can predict a patient's response to chemotherapy and radiation treatment.
One study demonstrated an AUC of 0.86 in the training cohort and 0.84 in the validation cohort, showcasing strong predictive performance. AI-assisted systems provide real-time guidance during surgical resection, enhancing safety and efficacy. AI-powered systems assist radiation oncologists with real-time imaging analysis and predictive analytics, optimizing radiotherapy dosages based on patient-specific data.
While AI demonstrates significant promise in NSCLC management, challenges such as data quality, model interpretability, and ethical considerations remain. Addressing these challenges will be crucial to fully leveraging AI's potential in precision medicine for NSCLC.