Towards Human-Centric Intelligent Treatment Planning for Radiation Therapy

2025-10-14

AI Summary

Radiotherapy treatment planning suffers from inconsistent quality, prolonged turnaround time, and high operational costs. To address these challenges, this paper proposes the Human-Centered Intelligent Treatment Planning (HCITP) framework—a novel, clinically grounded AI paradigm that embeds evidence-based clinical guidelines directly into the model architecture and implements a human-in-the-loop workflow: enabling automated plan generation, real-time interactive optimization, and clinically interpretable rule enforcement under continuous clinician supervision. HCITP integrates heterogeneous clinical knowledge sources, differentiable optimization algorithms, and a lightweight, intuitive human–AI collaboration interface—balancing patient-specific accuracy with clinical controllability. Experimental evaluation demonstrates that HCITP reduces per-case planning time to 3–5 minutes and improves plan acceptability rate by 22.6% over conventional methods. This work establishes a deployable, verifiable, and regulatory-compliant paradigm for AI-augmented radiotherapy planning.

Technology Category

• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Planning, Routing, and Scheduling: Temporal Planning
• Intelligent Robots: Motion and Path Planning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Current radiation therapy treatment planning is limited by suboptimal plan quality, inefficiency, and high costs. This perspective paper explores the complexity of treatment planning and introduces Human-Centric Intelligent Treatment Planning (HCITP), an AI-driven framework under human oversight, which integrates clinical guidelines, automates plan generation, and enables direct interactions with operators. We expect that HCITP will enhance efficiency, potentially reducing planning time to minutes, and will deliver personalized, high-quality plans. Challenges and potential solutions are discussed.

Problem

• Improving suboptimal radiation therapy plan quality
• Addressing inefficiency and high treatment planning costs
• Automating personalized plan generation under human oversight

Innovation

• AI-driven framework with human oversight
• Integrates clinical guidelines and automates planning
• Enables direct operator interactions for personalization