Leveraging Nano-Enabled AI Technologies for Cancer Prediction, Screening, and Detection

Abstract

Studies have recognized cancer as both a diverse and complex medical condition with multiple distinct characteristics. The correct early detection coupled with accurate diagnosis plays an essential role in executing clinical management techniques and it benefits survival rates. Healthcare benefits from the introduction of Artificial Intelligence (AI) alongside Machine Learning (ML) and Deep Learning (DL) which allows better cancer prediction capabilities. These statistical approaches process extensive data collections to reveal obscure systems that human beings have trouble identifying. The development of prediction models has been facilitated through AI algorithms where support vector machines (SVMs) and convolutional neural networks (CNNs) and artificial neural networks (ANNs) provide valuable enhancement to clinical decision processes in cancer research. This work studies how AI technology merges with developing nanotechnology by analyzing systems which combine AI principles with nanomaterial characteristics to detect cancer early then screen and treat cancer effectively. Our analysis explores recent developments in two-dimensional (2D) materials combined with smart sensors for cancer biomarker detection to establish diagnostic tools that bring accurate results through portable and affordable systems for clinical application. Cancer management is expected to experience a transformative change through these new innovations which support precise personalized treatment approaches. Widespread clinical use of these systems requires additional research because current technical limitations must be addressed in order to establish robust reliability.