Towards human-centric intelligent treatment planning for radiation therapy
Adnan Jafar, Xun Jia
TL;DR
This paper proposes an AI-based framework for radiation therapy planning that aims to improve efficiency and plan quality while involving human oversight.
Contribution
Introduces HCITP, a novel AI-driven treatment planning framework that integrates clinical guidelines and enables direct planner interaction.
Findings
HCITP could reduce planning time to minutes while maintaining high-quality plans.
The framework supports personalized treatment planning under human supervision.
Challenges in implementation and potential solutions are outlined.
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 interaction with planners. 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.
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Taxonomy
TopicsAdvanced Radiotherapy Techniques · AI-based Problem Solving and Planning · Robotic Path Planning Algorithms