Abstract
The article explores AI and ML's transformative potential in reshaping the radiation therapy landscape. The article navigates through the evolving field of radiation oncology, highlighting the constant influx of information facilitated by advanced imaging techniques. The technical scrutiny of AI's potential within radiation oncology is explored, contrasting definitions by Russell and Norvig with Goel's more insightful perspective. A detailed overview of the radiation therapy process, from diagnosis to follow-up, sets the stage for discussing the role of AI and ML. The utilities of AI in radiation oncology are dissected, emphasizing the reduction of clinical load through decision support systems, streamlined treatment planning, and the automated enhancement of radiation therapy. The article showcases various AI algorithms deployed in the workflow, their applications, and the promising results they offer. While acknowledging the challenges, including the opaque nature of AI and the critical need for clinical adoption, the article outlines criteria for evaluating AI tools in clinical settings. It stresses the importance of trust-building, transparency and overcoming challenges to harness AI's full potential in radiation oncology. In conclusion, the article advocates for a proactive integration of AI and ML, envisioning a future where these technologies empower radiation oncologists to enhance patient care, optimize workflows, and advance the field.
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