• VEERAVARAPRASAD PINDI

Artificial Intelligence (AI) is revolutionizing oncology treatment planning by leveraging advanced analytics to optimize patient care across multiple facets. This review examines AI's impact in patient stratification, radiation therapy planning, and drug selection. AI techniques integrate genetic profiles, biomarkers, and clinical data to stratify patients into subgroups, enabling personalized treatment plans that enhance efficacy and minimize side effects. In radiation therapy, AI analyzes imaging data to precisely target tumors while sparing healthy tissues, improving treatment precision and outcomes. Additionally, AI predicts treatment responses based on molecular data, optimizing drug selection and combination therapies through computational modeling. Despite these advancements, challenges such as data integration and regulatory hurdles remain. Future research should focus on refining AI algorithms, integrating emerging technologies, and validating AI-driven strategies in diverse patient populations to realize the full potential of AI in transforming oncology care towards personalized medicine.

Artificial Intelligence, oncology, treatment planning, drug selection, genomic data, machine learning, deep learning

"Optimizing Treatment Plans with AI: A Case Study in Oncology", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.K602-K611, June-2024, Available :http://www.jetir.org/papers/JETIR2406A72.pdf

"Optimizing Treatment Plans with AI: A Case Study in Oncology", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. ppK602-K611, June-2024, Available at : http://www.jetir.org/papers/JETIR2406A72.pdf