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Medicine

Large Language Models for AI-Assisted Radiotherapy Scheduling: A Feasibility Study Under Realistic Operational Constraints

Eric Zhang, Wen Li, Youfang Lai, Annette Souranis, Georgia Paparoidamis, Michael Roumeliotis, Xun Jia

Featured May 17, 2026

Original Paper

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Simply

Using smart computer programs that understand everyday language, this study shows they can create complex hospital treatment schedules, making sure all rules are followed and even suggesting fixes when things go wrong.

In depth
This study explores the use of Large Language Models (LLMs) to automate complex radiotherapy patient scheduling. Instead of traditional mathematical optimization, the authors leverage structured natural language prompts to encode clinical rules and operational constraints. This approach allows the LLM to generate feasible schedules, implicitly balancing multiple objectives like treatment-time consistency and machine utilization, and robustly handling infeasible scheduling requests by identifying conflicts and suggesting corrective actions.

Key Takeaways

  • 1
    The study demonstrates the feasibility of LLMs for generating complex radiotherapy schedules, satisfying numerous clinical and operational constraints in a simulated environment.
  • 2
    A novel structured natural language prompting framework enables LLMs to interpret and apply diverse clinical rules and adapt to changing operational priorities without requiring model reformulation.
  • 3
    The framework exhibits robust handling of infeasible requests, explicitly identifying constraint violations and proposing interpretable corrective actions, which is crucial for clinical translation.

Conceptual Flow

HIGH LEVEL
1
Methodology: LLM-Driven Scheduling

Instead of complex math, the computer is told the rules in plain words, then it figures out the best schedule.

Patient Needs
Clinic Rules
Machine Status
Understand and Plan
Daily Schedule
2
Results: Flexible and Reliable Schedules

The computer made good schedules that followed rules, kept treatment times steady, and even knew when a request couldn't be met.

LLM Schedule Plan
Check Rules and Adapt
Consistent Times
Less Machine Switching
Compact Schedules
Handles Bad Requests