Prospect of large language models and natural language processing for lung cancer diagnosis: A systematic review

Abstract Lung cancer, a leading cause of global mortality, demands a combat for its effective prevention, early diagnosis, and advanced treatment methods. Traditional diagnostic methods face limitations in accuracy and efficiency, necessitating innovative solutions. Large Language Models (LLMs) and Natural Language Processing (NLP) offer promising avenues for overcoming these challenges by providing comprehensive insights into medical data and personalizing treatment plans. This systematic review explores the transformative potential of LLMs and NLP in automating lung cancer diagnosis. It evaluates their applications, particularly in medical imaging and the interpretation of complex medical data, and assesses achievements and associated challenges. Emphasizing the critical role of Artificial Intelligence (AI) in medical imaging, the review highlights advancements in lung cancer screening and deep learning approaches. Furthermore, it underscores the importance of on‐going advancements in diagnostic methods and encourages further exploration in this field.