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

Abstract

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 non-standard 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.