The study of artificial intelligence (AI) in personalized radiotherapy planning for rare cancers manifests promising possibilities. Radiotherapy, utilizing high-energy ionizing radiation, has advanced with technological developments such as IMRT, SBRT, and IGRT, upgrading precision and facing challenges such as inherent cancer resistance. Rare cancers, accounting for 22% of global diagnoses, experience diagnostic difficulties and finite treatment alternatives. AI helps in automating radiotherapy planning, mitigating time and irregularity, and personalizing treatments by analyzing patient-specific data. AI utilization in oncology, such as machine learning and neural networks, intensifies diagnosis, treatment planning, and prognosis of treatment responses. Future directions incorporate addressing data insufficiency, fostering interdisciplinary participation, and overcoming technical and ethical challenges to enhance AI-based medical radiation therapy for rare cancers.

AI’s utilization in oncology, incorporating early diagnosis, personalized treatment, and predictive interpretations, signifies considerable developments in clinical outcomes. AI enhances medical imaging, combines various datasets, and aids decision-making procedures. Particularly, AI features motion tracking, automatic segmentation, and dose and outcome prediction, which are critical for rare cancer treatment. For example, AI applications have shown effectiveness in anticipating patient outcomes and enhancing radiotherapy plans, adjusting to the unique anatomical characteristics of each patient.