Hoskin P J
Mount Vernon Cancer Centre, Northwood, UK; Division of Cancer Sciences, University of Manchester, UK.
Clin Oncol (R Coll Radiol). 2025 Feb;38:103644. doi: 10.1016/j.clon.2024.09.003. Epub 2024 Sep 17.
Artificial intelligence (AI) is already an essential tool in the handling of large data sets in epidemiology and basic research. Significant contributions to radiological diagnosis are emerging alongside increasing use of digital pathology. The future lies in integrating this information together with clinical data relevant to each individual patient. Linkage with clinical protocols will enable personalized management options to be presented to the oncologist of the future. Radiotherapy has the distinction of being the first to have a National Institute for Health and Care Excellence (NICE)-approved AI-based recommendation. There is the opportunity to revolutionize the workflow with many tasks currently undertaken by clinicians taken over by AI-based systems for volume outlining, planning, and quality assurance. Education and training will be essential to understand the AI processes and inputs. Clinicians will however have to feel confident interrogating the AI-derived information and in communicating AI-derived treatment plans to patients.
人工智能（AI）已经成为流行病学和基础研究中处理大型数据集的重要工具。随着数字病理学的使用日益增加，其对放射诊断的重大贡献也不断涌现。未来在于将这些信息与每个患者的临床数据整合在一起。与临床方案的关联将使未来的肿瘤学家能够获得个性化的管理方案。放射治疗率先获得了英国国家卫生与临床优化研究所（NICE）批准的基于人工智能的建议。目前由临床医生执行的许多任务，如体积勾勒、治疗计划和质量保证等，都有机会由基于人工智能的系统接管，从而彻底改变工作流程。教育和培训对于理解人工智能的过程和输入至关重要。然而，临床医生必须有信心审视人工智能得出的信息，并将基于人工智能的治疗计划传达给患者。
