Macheka Sheba, Ng Peng Yun, Ginsburg Ophira, Hope Andrew, Sullivan Richard, Aggarwal Ajay
Institute of Cancer Policy, King's College London Faculty of Life Sciences & Medicine, London, UK.
National Cancer Institute Center for Global Health, Bethesda, Maryland, USA.
BMJ Oncol. 2024 May 10;3(1):e000255. doi: 10.1136/bmjonc-2023-000255. eCollection 2024.
The role of artificial intelligence (AI) in cancer care has evolved in the face of ageing population, workforce shortages and technological advancement. Despite recent uptake in AI research and adoption, the extent to which it improves quality, efficiency and equity of care beyond cancer diagnostics is uncertain to date. Henceforth, the objective of our systematic review is to assess the clinical readiness and deployability of AI through evaluation of prospective studies of AI in cancer care following diagnosis. We undertook a systematic review to determine the types of AI involved and their respective outcomes. A PubMed and Web of Science search between 1 January 2013 and 1 May 2023 identified 15 articles detailing prospective evaluation of AI in postdiagnostic cancer pathway. We appraised all studies using Risk of Bias Assessment of Randomised Controlled Trials and Risk of Bias In Non-randomised Studies-of Interventions quality assessment tools, as well as implementational analysis concerning time, cost and resource, to ascertain the quality of clinical evidence and real-world feasibility of AI. The results revealed that the majority of AI oncological research remained experimental without prospective clinical validation or deployment. Most studies failed to establish clinical validity and to translate measured AI efficacy into beneficial clinical outcomes. AI research are limited by lack of research standardisation and health system interoperability. Furthermore, implementational analysis and equity considerations of AI were largely missing. To overcome the triad of low-level clinical evidence, efficacy-outcome gap and incompatible research ecosystem for AI, future work should focus on multicollaborative AI implementation research designed and conducted in accordance with up-to-date research standards and local health systems.
面对人口老龄化、劳动力短缺和技术进步，人工智能（AI）在癌症护理中的作用不断演变。尽管近期人工智能研究和应用有所增加，但迄今为止，其在癌症诊断之外改善护理质量、效率和公平性的程度尚不确定。因此，我们系统评价的目的是通过评估人工智能在癌症诊断后护理中的前瞻性研究，来评估人工智能的临床准备情况和可部署性。我们进行了一项系统评价，以确定所涉及的人工智能类型及其各自的结果。在2013年1月1日至2023年5月1日期间对PubMed和科学网进行检索，共识别出15篇详细介绍人工智能在癌症诊断后路径中的前瞻性评估的文章。我们使用随机对照试验的偏倚风险评估和非随机干预研究的偏倚风险质量评估工具对所有研究进行评估，并对时间、成本和资源进行实施分析，以确定临床证据的质量和人工智能在现实世界中的可行性。结果显示，大多数人工智能肿瘤学研究仍处于实验阶段，没有进行前瞻性临床验证或部署。大多数研究未能确立临床有效性，也未能将测得的人工智能疗效转化为有益的临床结果。人工智能研究受到研究标准化不足和卫生系统互操作性的限制。此外，人工智能的实施分析和公平性考虑在很大程度上缺失。为了克服低水平临床证据、疗效-结果差距和人工智能研究生态系统不兼容这三重问题，未来的工作应侧重于按照最新研究标准和当地卫生系统设计和开展的多协作人工智能实施研究。
