AI assists in targeting NSCLC patients by non-invasively predicting c-MET overexpression status; DL models applied to hematoxylin and eosin (H&E) stained histopathology slides help in the classification and mutation prediction of NSCLC (26) as well as radiomics enables the extraction of high dimensional quantitative image features from routine imaging such as CT or PET scan that can capture tumor heterogeneity, enabling precise patient selection and stratification for targeted drug therapies while reducing the need for invasive biopsies (27). This evidence concerns the gene MET and non-small cell lung carcinoma.