Deep Learning Approaches to Enhancing Personalised Cancer Treatment

Abstract

Regardless of the variability of the disease and the individual variances among patients, cancer treatment has historically taken a standardised approach, which frequently leads to less than ideal results. By customising treatment plans to each patient's distinct genetic, phenotypic, and clinical characteristics, AI-driven personalised medicine has the promise of completely changing the way cancer is treated. This study suggests a thorough framework for predicting the best course of treatment for each patient by combining multi-omics data, electronic health records (EHRs), and empirical evidence. To analyse complicated datasets, the framework uses machine learning and deep learning models, such as Random Forests, Support Vector Machines, Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs). Model resilience is increased by creating synthetic data with Generative Adversarial Networks (GANs). In order to find possible indicators for therapy response and resistance, AI is also utilised in biomarker identification. Personalised treatment plans are recommended by a decision-support system using AI predictions, and the model's accuracy is continuously improved through feedback loops. This AI-driven strategy dramatically improves patient outcomes, according to preliminary studies, which represents a promising development in tailored cancer care.