Interactive large language model-assistant for flexible workflow automation in radiotherapy

Purpose: Automated scripts and workflows have been implemented in clinics to streamline the planning process, improving efficiency and consistency.However, standardized scripts often lack adaptability for patient-specific scenarios, requiring considerable effort to modify for nonstandard cases.To address this, we present an interactive large language model (LLM)-driven approach for flexible workflow automation across radiation oncology tasks.This work presents a proof-of-concept agentic LLM integration that enables flexible, natural-language automation across a broad set of radiotherapy (RT) workflow operations.Methods: An LLM-based assistant system was integrated into the MIM software platform.It includes a recursive MIM workflow, an agentic orchestrator, and coordinated agents: an LLM Consultant for selecting relevant functions, a code generator that compiles executable Java extensions, a Quality Checker for independent verification, and a Knowledge Accumulator that captures and stores valuable insights such as coding patterns, errors, and user preferences.The system uses a prompt-based approach with continuous learning from both successful executions and error corrections to enhance accuracy and adaptability.Its generalizability was validated using 57 realistic simple queries, robustness through repeatability and failure-rate testing, and overall performance through four complex examples addressing advanced clinical tasks across various stages of the adaptive RT workflow.Results: The system effectively replicated standard clinical workflows with high adaptability and flexibility.Early queries required extensive function library accumulation, while later ones mainly reused existing functions.Its multi-agent architecture enabled robust error recovery, with automatic correction loops reducing failure rates from 1% to near zero.Average execution time per query was 13-14 s.All complex examples were successfully implemented in MIM, supporting interactive use, dynamic workflow customization, and straightforward execution.Conclusion: By integrating an interactive AI assistant, the novel LLM-powered tool provides crucial workflow flexibility alongside automation-reducing workflow rigidity, enhancing efficiency, and promising a paradigm shift toward dynamic, patient-specific treatment planning and data management.