2026-10-27 · ArXiv
arXiv:2610.06923v2 Announce Type: replace
Abstract: Artificial intelligence has advanced radiotherapy tasks, but these capabilities remain fragmented across the care pathway. Here we present RadOnc-Agent, an integrated framework connecting 17 phase-specific functions and 9 cross-cutting utilities across four phases through a conversational interface. A language-model controller maps clinical intent to structured calls, maintains longitudinal context and validates dispatch. We evaluated request-expression diversity, function coverage, cross-stage composition, information transfer and failure recovery using 2,600 single-function requests (7,800 executions), 200 cross-stage scenarios (600 executions), and 120 workflows from 60 de-identified patient records (360 executions). RadOnc-Agent selected the intended function in 98.79% of single-function executions and completed 96.50% of scripted and 96.67% of real-patient workflows. Removing longitudinal state reduced cross-stage completion from 96.50% to 84.00%; bypassing schema and identity validation increased mismatched dispatch from 0% to 95.28% in replay/test execution. Single-function performance did not by itself predict workflow-level capability. Information continuity and cross-stage dependencies emerged as central constraints. These results establish the technical feasibility of longitudinal integration across radiotherapy workflows and provide a foundation for prospective clinical evaluation.
原文链接：https://arxiv.org/abs/2610.06923
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