A Review of AI-Driven Methods for Cytotoxicity Analysis and Predictive Modeling in the Treatment of Cancer Using Nanomaterials
Aakash Prabhu, V.S. Selvakumar · 2025
The exceptional characteristics of nanomaterials, such as improved therapeutic outcomes, focused therapy, and enhanced drug delivery, have made them particularly promising in the fight against cancer. Nevertheless, nanoparticles' cytotoxicity is a major concern that needs careful assessment before they may be used in therapeutic settings. Novel methods for predicting cytotoxicity, optimizing treatment tactics, and improving the understanding of nanomaterial interactions with biological systems have been made possible by the advent of artificial intelligence (AI), which has completely transformed the area of biomedical research. With nanomaterials as a treatment for cancer, this paper seeks to give a thorough summary of AI-driven approaches used for cytotoxicity studies and predictive modeling. We investigate many ML and DL methods, such as support vector machines (SVMs), random forests (RFs), artificial neural networks (ANNs), and convolutional neural networks (CNNs), that have been created to simulate nanomaterial-induced cytotoxicity. We also look at how physicochemical characteristics of nanomaterials, gene expression profiles, and proteomics data might be combined with AI technologies to enhance therapeutic results and prediction accuracy. Predicting dose-response connections, identifying biocompatibility of nanomaterials, and real-time monitoring of cellular responses to nanomaterials are only a few of the AI applications included in the paper that pertain to cancer nanomedicine. Data heterogeneity, model interpretability, and the absence of defined evaluation processes are some of the main obstacles and constraints that AI models encounter in this field. To sum up, a revolutionary strategy for improving the security and effectiveness of cancer treatments based on nanomaterials is to employ AI-driven methodologies for cytotoxicity study and predictive modeling. The study highlights the importance of interdisciplinary collaboration to tackle present issues and fully utilize AI's potential in cancer nanomedicine, which is a fast expanding subject. It offers significant insights into the current state and future possibilities of AI in this area.