School of Engineering, Cardiff University, UK.
Velindre Cancer Centre, UK.
Radiography (Lond). 2021 Oct;27 Suppl 1:S63-S68. doi: 10.1016/j.radi.2021.07.012. Epub 2021 Sep 4.
Radiation oncology is a continually evolving speciality. With the development of new imaging modalities and advanced imaging processing techniques, there is an increasing amount of data available to practitioners. In this narrative review, Artificial Intelligence (AI) is used as a reference to machine learning, and its potential, along with current problems in the field of radiation oncology, are considered from a technical position.
AI has the potential to harness the availability of data for improving patient outcomes, reducing toxicity, and easing clinical burdens. However, problems including the requirement of complexity of data, undefined core outcomes and limited generalisability are apparent.
This original review highlights considerations for the radiotherapy workforce, particularly therapeutic radiographers, as there will be an increasing requirement for their familiarity with AI due to their unique position as the interface between imaging technology and patients.
Collaboration between AI experts and the radiotherapy workforce are required to overcome current issues before clinical adoption. The development of educational resources and standardised reporting of AI studies may help facilitate this.
放射肿瘤学是一个不断发展的专业。随着新的成像方式和先进的成像处理技术的发展，可供从业者使用的数据量越来越多。在这篇叙述性综述中，人工智能（AI）被用作机器学习的参考，从技术角度考虑了它在放射肿瘤学领域的潜在应用以及当前存在的问题。
人工智能有可能利用数据的可用性来改善患者的预后，降低毒性，并减轻临床负担。然而，包括数据复杂性的要求、核心结果不明确和通用性有限在内的问题是显而易见的。
这篇原创综述强调了放疗人员（特别是治疗放射技师）的考虑因素，由于他们作为影像学技术和患者之间的接口的独特地位，他们将越来越需要熟悉人工智能，因此这对他们具有一定的指导意义。
在临床应用之前，需要人工智能专家和放疗人员之间的合作来克服当前的问题。开发教育资源和人工智能研究的标准化报告可能有助于促进这一点。
