Transforming Stereotactic Radiosurgery with Artificial Intelligence: Toward Intelligent Planning and Streamlined Workflows

Stereotactic radiosurgery (SRS) and hypofractionated stereotactic radiotherapy (hSRT) have become a cornerstone in the management of intracranial lesions, offering high precision and ablative dose delivery in one or a limited number of treatment fractions. As clinical indications continue to expand, patients are increasingly treated for larger numbers of brain metastases in a single course and repeat SRS/hSRT courses for new and/or recurrent lesions have become more common. These evolving treatment paradigms substantially increase planning complexity, as target delineation, cumulative dose assessment, normal tissue sparing, and treatment optimization become increasingly challenging with each additional lesion and prior course of radiation. Despite its clinical success, SRS/hSRT treatment planning and delivery remain resource-intensive, requiring expert-driven target and normal structure contouring, treatment plan optimization, and rigorous quality assurance. Recent advances in artificial intelligence (AI) present an opportunity to enhance the efficiency, consistency, and patient-specific personalization of SRS/hSRT workflows while enabling the safe and scalable implementation of increasingly complex clinical applications. This review examines the current and emerging applications of AI across the entire SRS/hSRT treatment delivery pipeline, including automated image segmentation, delineation of targets and normal tissues, treatment planning, and dose prediction. Additionally, we will explore AI-enabled workflow integration and clinical decision support systems that facilitate multidisciplinary collaboration. Challenges to widespread adoption, such as data inhomogeneity, model interpretability, regulatory considerations, and the need for clinical validation, will also be explored. By summarizing current evidence and identifying key research directions, this review outlines a path toward intelligent, streamlined SRS/hSRT workflows in which AI augments clinical expertise and ultimately improves patient outcomes.