Non-small cell lung carcinoma (NSCLC) is a prevalent and aggressive form of lung cancer, with a poor prognosis for metastatic disease. Immunotherapy, particularly immune checkpoint inhibitors (ICIs), has revolutionized the management of NSCLC, but response rates are highly variable. Identifying reliable predictive biomarkers is crucial to optimize patient selection and treatment outcomes. This systematic review aimed to evaluate the current state of artificial intelligence (AI) and machine learning (ML) applications in predicting the response to immunotherapy in NSCLC. A comprehensive literature search identified 19 studies that met the inclusion criteria. The studies employed diverse AI/ML techniques, including deep learning, artificial neural networks, support vector machines, and gradient boosting methods, applied to various data modalities such as medical imaging, genomic data, clinical variables, and immunohistochemical markers. Several studies demonstrated the ability of AI/ML models to accurately predict immunotherapy response, progression-free survival, and overall survival in NSCLC patients. However, challenges remain in data availability, quality, and interpretability of these models. Efforts have been made to develop interpretable AI/ML techniques, but further research is needed to improve transparency and explainability. Additionally, translating AI/ML models from research settings to clinical practice poses challenges related to regulatory approval, data privacy, and integration into existing healthcare systems. Nonetheless, the successful implementation of AI/ML models could enable personalized treatment strategies, improve treatment outcomes, and reduce unnecessary toxicities and healthcare costs associated with ineffective treatments.

The discovery of immune checkpoint inhibitors (ICIs) has revolutionized the care of cancer patients. However, the response to ICI therapy exhibits substantial interindividual variability. Efforts have been directed to identify biomarkers that predict the clinical response to ICIs. In recent years, the gut microbiome has emerged as a critical player that influences the efficacy of immunotherapy. An increasing number of studies have suggested that the baseline composition of a patient\'s gut microbiota and its dysbiosis are correlated with the outcome of cancer immunotherapy. This review tackles the rapidly growing body of evidence evaluating the relationship between the gut microbiome and the response to ICI therapy. Additionally, this review highlights the impact of antibiotic-induced dysbiosis on ICI efficacy and discusses the possible therapeutic interventions to optimize the gut microbiota composition to augment immunotherapy efficacy.