Leveraging Artificial Intelligence (AI) Methods for Non-Small Cell Lung Cancer (NSCLC) Detection: A Review - Faisal Asadi; Ashraf Alif Adillah; Jonathan Lucas Fontana; Michael Dimas Chrispradipta; Mousa Khalil Mousa Ayesh; Wairanatha Halim (Engineering, MAthematics and Computer Science Journal (EMACS), 2026)
One of the main causes of cancer-death that significant to global public health concern is lung cancer. NSCLC has been categorized as a burden disease, with an estimated reaching 85% of all lung cancer cases around the world. The problem was that NSCLC disease could only be detected when the disease has grown at a late stage. Therefore, AI technology is also being implemented to handle NSCLC disease. This review discusses how AI has played a role in treating NSCLC disease in the last five years of research journals that collected 5 years between 2019-2024. The database resources are from PubMed, Scopus, and Google Scholar. The process of selecting journal papers was analyzed based on an in-depth understanding of NSCLC disease journals as considered an inclusion criterion. This review used the PRISMA to analysis and review 17 journals. After carrying out the analysis process on the AI-NSCLC journals, we found that AI has been able to help humans respond to cases of NSCLC patients, starting from the detection stage, comprehensive diagnosis, and providing treatment recommendations. Treatments of NSCLC tend to be more personalized and could run more effectively and efficiently based on medical images input into the AI model. However, considering the urgency and vulnerability of the application of these AI models, which will be directly related to human health, the medical images dataset is also quite limited and the biggest challenge for AI-NSCLC.