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