Benjamin Movsas, MD

To evaluate agreement between LLMs and expert reviewers in triaging radiation oncology incident learning system (ILS) forms with regard to three clinically relevant dimensions (workflow process step, severity, and dosimetric impact), with the goal of improvin...

Dynamic contrast-enhanced (DCE)-MRI is widely used to assess vascular perfusion and permeability; however, conventional time-domain pharmacokinetic models often conflate flow- and leakage-driven transport. This study introduces a Laplace-domain framework to q...

Automated stereotactic radiosurgery (SRS) planning often fails to match the high conformity and complex trade-off logic of expert human dosimetrists. We hypothesized that a reasoning-based AI agent, SAGE (Secure Agent for Generative dose Expertise), could gen...

Current automated offline triggers for adaptive radiotherapy often function as black boxes and fail to provide the reasoning behind a decision. Vision Language Models (VLM) offer a novel solution by providing a clear path toward explainability regarding the d...

Radiotherapy planning is time-intensive, iterative, and operator dependent. AI automates planning but is not transparent, and cloud-based models threaten privacy. We developed and evaluated SAGE (Secure Agent for Generative dose Expertise), a locally hosted,...

Dynamic-contrast–enhanced (DCE) MRI enables quantitative assessment of microvascular permeability but is constrained at low field by reduced SNR and ambiguous voxel-wise model selection. This study constructs and evaluates an unsupervised probabilistic-nested...