Artificial intelligence use in lung cancer screening to improve patient recovery

Abstract

Artificial Intelligence (AI) is a method of computation that utilizes experience or data to make certain predictions. This paper explores the utilization of artificial intelligence (AI) in lung cancer screening to enhance patient recovery by facilitating early detection, personalized treatment, predictive analytics, and decision support. The implementation of AI algorithms in critical care settings aims to monitor patient data for early complication detection, create personalized treatment plans, predict patient outcomes, and provide decision support for healthcare providers. Lung cancer screening, particularly through Low-Dose Computed Tomography (LDCT) scans, has been proven effective in improving long-term survival rates by detecting lung cancer at early stages. AI can significantly enhance the accuracy and efficiency of lung cancer screening by analyzing Computed Tomography (CT) images, assessing patient risk factors, and providing decision support for radiologists. Various machine learning algorithms such as KNN, SVM, CNN, and FFNN have been employed and evaluated for their performance in lung cancer detection, with CNN and FFNN demonstrating higher accuracy and sensitivity.