Suppr超能文献

NRG肿瘤学对人工智能在放射治疗临床试验自动治疗计划中的评估:现状与未来。

NRG Oncology Assessment of Artificial Intelligence for Automatic Treatment Planning in Radiation Therapy Clinical Trials: Present and Future.

作者信息

Jia Xun, Rong Yi, Wu Qingrong, Cardenas Carlos E, Court Laurence E, Hrinivich William T, Kang Hyejoo, Kovalchuk Nataliya, Whitaker Thomas J, Xiao Ying, Zhang Pengpeng, Chen Quan

机构信息

Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland.

Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona.

出版信息

Int J Radiat Oncol Biol Phys. 2025 Mar 29. doi: 10.1016/j.ijrobp.2025.03.045.

Abstract

PURPOSE

Recent advances in artificial intelligence (AI) have showcased the potential of automatic treatment planning for clinical trials involving radiation therapy. This paper offers an overview of the current landscape of AI-based treatment planning, emphasizing its ability to improve plan quality and streamline the planning process.

METHODS AND MATERIALS

Acknowledging the increasing clinical utilization and promise of these technologies, the NRG Oncology Medical Physcis Subcommittee established a working group to assess the status of AI-based automatic treatment planning for clinical trials, along with its challenges and future directions.

RESULTS

We describe its critical roles within radiation therapy clinical trials and discuss the challenges of integrating AI into such settings. We further outline short-term actions for enhancing AI-based automatic treatment planning for radiation therapy clinical trials and explore future directions for the field, such as the development of personalized algorithms, the integration of AI into routine clinical practice, and the need for support in this direction.

CONCLUSIONS

This assessment provides insights into the present state and prospects of AI in radiation therapy clinical trials to facilitate enhanced treatment planning and patient care.

摘要

目的

人工智能(AI)的最新进展展现了其在涉及放射治疗的临床试验中进行自动治疗计划的潜力。本文概述了基于AI的治疗计划的当前状况,强调了其改善计划质量和简化计划流程的能力。

方法与材料

鉴于这些技术在临床上的应用日益广泛且前景广阔,NRG肿瘤学医学物理小组委员会成立了一个工作组,以评估基于AI的临床试验自动治疗计划的现状及其面临的挑战和未来方向。

结果

我们描述了其在放射治疗临床试验中的关键作用,并讨论了将AI整合到此类环境中的挑战。我们进一步概述了加强放射治疗临床试验基于AI的自动治疗计划的短期行动,并探索该领域的未来方向,例如个性化算法的开发、将AI整合到常规临床实践中以及在这方面提供支持的必要性。

结论

本评估提供了对AI在放射治疗临床试验中的现状和前景的见解,以促进改进治疗计划和患者护理。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验