The role of artificial intelligence in personalized oncology: predictive models and treatment optimization

Abstract

In recent years, artificial intelligence (AI) has revolutionized the field of oncology by introducing data-driven approaches for personalized diagnosis and treatment. Predictive AI models based on genetic, molecular, and clinical data enable oncologists to identify individual tumor characteristics and optimize therapeutic strategies. Machine learning algorithms such as deep neural networks, random forests, and support vector machines have shown remarkable accuracy in predicting treatment response, drug resistance, and disease progression. Furthermore, AI systems can integrate radiological and histopathological data to enhance precision in cancer staging and therapy planning. Despite the great potential, challenges remain in data standardization, model interpretability, and ethical issues related to patient privacy. This paper aims to analyze the current role of AI in personalized oncology and explore the emerging opportunities for improving predictive models and treatment outcomes.