AI-Driven Framework for Early Cancer Detection and Diagnosis

This research investigates the potential applications of artificial intelligence (AI) in the early detection and growth prediction of various cancer types. By analyzing patient data, it identifies significant risk factors such as age, gender, and underlying medical conditions. Advanced AI techniques, including artificial neural networks (ANN) and logistic regression and a Large Language Model (LLM), are utilized, with the ANN model demonstrating strong performance metrics—sensitivity at 0.757, specificity at 0.755, and an area under the curve (AUC) of 0.873. LLM enhances readability and provides information on cancer diagnosis. The study underscores AI's transformative role in cancer management, encompassing diagnosis, treatment, drug development, and postoperative care. It also addresses challenges like standardizing AI protocols and minimizing image variability, particularly in ultrasound imaging. Finally, the research highlights the importance of ethical AI implementation and ongoing innovation to improve clinical outcomes. Artificial Intelligence, Early Detection, Cancer Prediction, Artificial Neural Networks (ANN), Logistic Regression, Sensitivity, Specificity, Area Under the Curve (AUC), Risk Factors, Cancer Management, Diagnosis.