Fig. 4From: The artificial intelligence and machine learning in lung cancer immunotherapyA Methods of lung cancer immunotherapy prediction. The application of AI-based technologies in lung cancer immunotherapy can process radiomics images, pathology images, genetics information, epigenetic information, microbiology information, hematology values, proteomics information, multi-omics data and so on. AI can use diverse data to predict immunotherapy benefits in lung cancer patients. B Al predicts lung cancer immunotherapy adverse effects. Abbreviation: irAEs: immune-related adverse events; BMI: Body mass index; ECOG PS: Eastern Cooperative Oncology Group performance status; NLR: Neutrophil to lymphocyte ratio; ALB: Albumin; PLR: Platelet-to-lymphocyte ratio; TSH: Thyroid-stimulating hormone; LDH: Lactate dehydrogenase. Created with BioRender.comBack to article page