Abstract
Artificial intelligence (AI) has revolutionized motion-adaptive radiotherapy (ART) by enhancing tumor-tracking accuracy and optimizing radiation dosage delivery. Traditional motion management techniques, such as respiratory gating and internal target volume (ITV) expansion, often result in increased treatment margins and unintended radiation exposure. AI-powered real-time motion tracking, deformable image registration (DIR), and ART offer superior tumor localization, automated dose modulation, and real-time imaging integration. This study examined AI-based motion guidance technologies in helical tomotherapy (HT) and CyberKnife (Accuray, Madison, WI) robotic radiosurgery, highlighting technical innovations, engineering challenges, and clinical applications. HT employs megavoltage computed tomography (MVCT) for intra-fractional motion monitoring, whereas CyberKnife utilizes x-ray-based beam correction via a robotic arm to achieve submillimeter precision. Despite advancements, challenges such as AI processing latency, tumor motion variability, and multimodal imaging integration persist. Future research should focus on improving the AI response times, enhancing motion prediction algorithms, and developing fully automated AI-based radiation delivery systems.