Primary Care Unit, Department of Public Health & Primary Care, University of Cambridge, Cambridge, United Kingdom.
Wolfson Institute for Preventive Medicine, Queen Mary University of London, London, United Kingdom.
J Med Internet Res. 2021 Mar 3;23(3):e23483. doi: 10.2196/23483.
More than 17 million people worldwide, including 360,000 people in the United Kingdom, were diagnosed with cancer in 2018. Cancer prognosis and disease burden are highly dependent on the disease stage at diagnosis. Most people diagnosed with cancer first present in primary care settings, where improved assessment of the (often vague) presenting symptoms of cancer could lead to earlier detection and improved outcomes for patients. There is accumulating evidence that artificial intelligence (AI) can assist clinicians in making better clinical decisions in some areas of health care.
This study aimed to systematically review AI techniques that may facilitate earlier diagnosis of cancer and could be applied to primary care electronic health record (EHR) data. The quality of the evidence, the phase of development the AI techniques have reached, the gaps that exist in the evidence, and the potential for use in primary care were evaluated.
We searched MEDLINE, Embase, SCOPUS, and Web of Science databases from January 01, 2000, to June 11, 2019, and included all studies providing evidence for the accuracy or effectiveness of applying AI techniques for the early detection of cancer, which may be applicable to primary care EHRs. We included all study designs in all settings and languages. These searches were extended through a scoping review of AI-based commercial technologies. The main outcomes assessed were measures of diagnostic accuracy for cancer.
We identified 10,456 studies; 16 studies met the inclusion criteria, representing the data of 3,862,910 patients. A total of 13 studies described the initial development and testing of AI algorithms, and 3 studies described the validation of an AI algorithm in independent data sets. One study was based on prospectively collected data; only 3 studies were based on primary care data. We found no data on implementation barriers or cost-effectiveness. Risk of bias assessment highlighted a wide range of study quality. The additional scoping review of commercial AI technologies identified 21 technologies, only 1 meeting our inclusion criteria. Meta-analysis was not undertaken because of the heterogeneity of AI modalities, data set characteristics, and outcome measures.
AI techniques have been applied to EHR-type data to facilitate early diagnosis of cancer, but their use in primary care settings is still at an early stage of maturity. Further evidence is needed on their performance using primary care data, implementation barriers, and cost-effectiveness before widespread adoption into routine primary care clinical practice can be recommended.
2018 年，全球有超过 1700 万人，包括英国的 36 万人被诊断患有癌症。癌症的预后和疾病负担在很大程度上取决于诊断时的疾病阶段。大多数被诊断患有癌症的人首先出现在初级保健环境中，在这些环境中，对癌症（通常是模糊）表现症状的评估得到改善，可能会导致更早的检测，并改善患者的治疗效果。有越来越多的证据表明，人工智能（AI）可以帮助临床医生在某些医疗保健领域做出更好的临床决策。
本研究旨在系统地综述可能有助于更早诊断癌症的 AI 技术，并可应用于初级保健电子健康记录（EHR）数据。评估证据的质量、AI 技术的发展阶段、证据中存在的差距以及在初级保健中的应用潜力。
我们检索了 MEDLINE、Embase、SCOPUS 和 Web of Science 数据库，检索时间从 2000 年 1 月 1 日至 2019 年 6 月 11 日，纳入了所有提供 AI 技术在癌症早期检测中应用的准确性或有效性证据的研究，这些技术可能适用于初级保健 EHR。我们纳入了所有研究设计和所有语言的研究。这些检索通过对基于 AI 的商业技术的范围综述进行扩展。主要评估结果是癌症诊断准确性的测量指标。
我们共检索到 10456 篇研究，16 篇研究符合纳入标准，共纳入 3862910 名患者的数据。共 13 项研究描述了 AI 算法的初始开发和测试，3 项研究描述了 AI 算法在独立数据集的验证。1 项研究基于前瞻性收集的数据，只有 3 项研究基于初级保健数据。我们没有发现关于实施障碍或成本效益的数据。偏倚风险评估突出了研究质量的广泛差异。对商业 AI 技术的额外范围综述确定了 21 项技术，只有 1 项符合我们的纳入标准。由于 AI 模式、数据集特征和结果测量的异质性，未进行荟萃分析。
AI 技术已应用于 EHR 类型的数据，以促进癌症的早期诊断，但在初级保健环境中的应用仍处于成熟的早期阶段。在广泛应用于常规初级保健临床实践之前，还需要更多关于其使用初级保健数据、实施障碍和成本效益的证据。
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