Accurate Diagnostics of Lung Cancer Using Prime Model Generative AI

Health informatics plays a crucial role in the early detection of lung cancer by enhancing the collection, analysis, and application of patient data in clinical settings. It enables the integration of data from electronic health records (EHRs), imaging, pathology reports, and even genomic information. Artificial Intelligence (AI) and Machine Learning (ML) technologies further support lung cancer detection by tracking disease progression over time, identifying changes that may suggest malignancy, and reducing false-positive and unnecessary procedures. A fundamental challenge, however, remains: many existing lung cancer prediction models report accuracy below 80%, emphasizing the need for more effective classification techniques. In this work, we introduce a novel approach that significantly improves predictive accuracy, achieving rates between 95% and 98% a notable advancement over current methods using the same dataset. This improvement is driven by a recently developed Generative AI technology, considered one of the most powerful tools for enhancing the performance of health informatics systems.