Artificial Intelligence in Radiotherapy: From Technical Automation to Assisted Clinical Decision-Making. A Literature Review

Abstract

Artificial intelligence (AI) is transforming the radiotherapy landscape by addressing challenges related to efficiency, standardization, and treatment personalization. This literature review critically synthesizes current and emerging applications of AI across the radiotherapy care continuum. We analyze evidence of its impact on four key areas: automatic segmentation, treatment planning, radiomics for prediction, and quality control. The data demonstrate substantial gains in reproducibility and operational efficiency. However, major obstacles to clinical implementation persist, including the need for robust prospective validation, the lack of transparency in algorithms ("black box" nature), risks of bias, and ethical-legal issues. We conclude that AI is destined to become an indispensable "co-pilot" for the radiation oncologist, but its successful integration will require rigorous validation frameworks, ethical governance, and an evolution of professional skills to prioritize patient safety and benefit.

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