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首页> 外文期刊>JMIR Medical Informatics >Artificial Intelligence Versus Clinicians in Disease Diagnosis: Systematic Review
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Artificial Intelligence Versus Clinicians in Disease Diagnosis: Systematic Review

机译：人工智能与疾病诊断临床医生：系统评论

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Background Artificial intelligence (AI) has been extensively used in a range of medical fields to promote therapeutic development. The development of diverse AI techniques has also contributed to early detections, disease diagnoses, and referral management. However, concerns about the value of advanced AI in disease diagnosis have been raised by health care professionals, medical service providers, and health policy decision makers. Objective This review aimed to systematically examine the literature, in particular, focusing on the performance comparison between advanced AI and human clinicians to provide an up-to-date summary regarding the extent of the application of AI to disease diagnoses. By doing so, this review discussed the relationship between the current advanced AI development and clinicians with respect to disease diagnosis and thus therapeutic development in the long run. Methods We systematically searched articles published between January 2000 and March 2019 following the Preferred Reporting Items for Systematic reviews and Meta-Analysis in the following databases: Scopus, PubMed, CINAHL, Web of Science, and the Cochrane Library. According to the preset inclusion and exclusion criteria, only articles comparing the medical performance between advanced AI and human experts were considered. Results A total of 9 articles were identified. A convolutional neural network was the commonly applied advanced AI technology. Owing to the variation in medical fields, there is a distinction between individual studies in terms of classification, labeling, training process, dataset size, and algorithm validation of AI. Performance indices reported in articles included diagnostic accuracy, weighted errors, false-positive rate, sensitivity, specificity, and the area under the receiver operating characteristic curve. The results showed that the performance of AI was at par with that of clinicians and exceeded that of clinicians with less experience. Conclusions Current AI development has a diagnostic performance that is comparable with medical experts, especially in image recognition-related fields. Further studies can be extended to other types of medical imaging such as magnetic resonance imaging and other medical practices unrelated to images. With the continued development of AI-assisted technologies, the clinical implications underpinned by clinicians’ experience and guided by patient-centered health care principle should be constantly considered in future AI-related and other technology-based medical research.
机译：背景技术人工智能（AI）已广泛用于一系列医疗领域，以促进治疗发展。多种AI技术的发展也有助于早期检测，疾病诊断和转诊管理。然而，对疾病诊断中晚期AI的价值的担忧由医疗保健专业人员，医疗服务提供者和健康政策决策者提出。目的旨在系统地审查文献，特别是专注于先进的AI和人类临床医生之间的性能比较，以提供关于AI对疾病诊断的应用程度的最新摘要。通过这样做，本综述讨论了目前高级AI开发和临床医生之间的关系，疾病诊断，从而长期治疗发展。方法在下列数据库中的系统评论和Meta分析的首选报告项目中，我们系统地搜索了2019年1月至2019年3月至2019年3月之间的文章：Scopus，PubMed，Cinahl，科学网站和Cochrane图书馆。根据预设的包含和排除标准，只考虑了比较高级AI和人类专家之间的医疗表现的文章。结果共鉴定了共有9篇文章。卷积神经网络是常用的先进AI技术。由于医疗领域的变化，在分类，标签，培训过程，数据集大小和AI的算法验证方面存在各个研究。文章中报告的性能指数包括诊断准确性，加权误​​差，假阳性率，灵敏度，特异性和接收器操作特征曲线下的区域。结果表明，AI的表现与临床医生的表现相当，超过了临床医生的经验。结论目前的AI开发具有与医学专家相当的诊断性能，特别是在图像识别相关领域。进一步的研究可以扩展到其他类型的医学成像，例如磁共振成像和与图像无关的其他医学实践。随着AI辅助技术的持续发展，临床医生经验和患者以患者为中心的医疗保健原则为基础的临床影响应在未来的AI相关和其他基于技术的医学研究中不断考虑。

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