This study addresses the implementation of AI-based models in the management of lung cancer. Nowadays, AI tools have become pervasive in our daily lives. Consider voice recognition tool Siri, self-driving cars, and face recognition systems, to name a few. Furthermore, it is essential to acknowledge the prominence and recent discussions surrounding groundbreaking AI tools such as ChatGPT. With the emergence of ChatGPT, everything seemed possible. Simultaneously, many concerns rose. Can humans handle the rise of such advanced AI systems? It soon became evident that ethical guidelines, transparency, education, and appropriate regulations were required. The key is to exploit AI’s potential while minimizing its negative consequences and ensuring that it serves the best interests of society. AI enters every field, as well as the healthcare sector. This study attempted to provide the numerous possibilities of AI in the management of lung cancer. Lung cancer is a severe disease afflicting patients around the world. Therefore, early detection, correct classification, and diagnosis are key. In addition, with the help of AI models, doctors and healthcare workers aim to improve the management of lung cancer. The studies in this paper have proven the added value of AI models in the screening, classification, diagnostic, prognostic, and treatment process. To build an AI model, large amounts of high-quality datasets related to lung cancer are necessary. These datasets can include CT-images, histopathological slices, etc. Thereafter, the AI model is trained, tested and validated using those datasets. Artificial intelligence techniques are capable of extracting information (features) from histopathological tissue and CT-images. These features could be shape, texture, characteristics etc. Then, AI models have the ability to link combinations of features to certain disease outcomes/disease conditions. This way, AI models are capable of predicting lung cancer risk, overall survival and treatment response. Moreover, these models can assist healthcare works in making diagnoses, prognoses and lung cancer classifications. Several studies demonstrated that the use of AI could significantly improve the performance of practicing doctors. Furthermore, AI models can increase the prediction accuracy. Additionally, by leveraging specific features and genomic datasets, these models can perform predictions of gene mutations. The identification of gene mutations is particularly significant in guiding drug selection for individuals with lung cancer. This way, treatment response can be better estimated. Next, the incorporation of clinical factors such as age, gender, smoking status and family history of cancer notably improved the performance of AI-models. It is clear, that AI tools have the potential to enhance the efficiency, accuracy, and accessibility of healthcare services. It allows healthcare providers to focus more on the patient’s care and decision-making while AI models assist and support doctors in their clinical expertise. However, the human aspect remains important in the doctor-patient relationship. Until today, doctors are still better at handling complex cases in comparison to AI models. This is why, AI models are tools to assist doctors rather than to replace them.