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基于人工智能干预的临床试验研究进展

许璐 王胜锋 詹思延

许璐, 王胜锋, 詹思延. 基于人工智能干预的临床试验研究进展[J]. 中华疾病控制杂志, 2021, 25(1): 12-15, 36. doi: 10.16462/j.cnki.zhjbkz.2021.01.003
引用本文: 许璐, 王胜锋, 詹思延. 基于人工智能干预的临床试验研究进展[J]. 中华疾病控制杂志, 2021, 25(1): 12-15, 36. doi: 10.16462/j.cnki.zhjbkz.2021.01.003
XU Lu, WANG Sheng-feng, ZHAN Si-yan. Research progress of clinical trials with artificial intelligence intervention[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2021, 25(1): 12-15, 36. doi: 10.16462/j.cnki.zhjbkz.2021.01.003
Citation: XU Lu, WANG Sheng-feng, ZHAN Si-yan. Research progress of clinical trials with artificial intelligence intervention[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2021, 25(1): 12-15, 36. doi: 10.16462/j.cnki.zhjbkz.2021.01.003

基于人工智能干预的临床试验研究进展

doi: 10.16462/j.cnki.zhjbkz.2021.01.003
基金项目: 

国家自然科学基金 81973146

国家自然科学基金 81502884

详细信息
    通讯作者:

    詹思延,E-mail:siyan-zhan@bjmu.edu.cn

    王胜锋,E-mail:shengfeng1984@126.com

  • 中图分类号: R181.2

Research progress of clinical trials with artificial intelligence intervention

Funds: 

National Natural Science Foundation of China 81973146

National Natural Science Foundation of China 81502884

More Information
  • 摘要: 随着越来越多的人工智能技术被开发应用于医疗卫生领域,近年来以人工智能为干预的临床试验开始不断出现。本文将介绍以人工智能为干预措施的临床试验的发展现状、方案指南、报告规范和面临的挑战与展望,以便于未来研究者规范地开展此类临床试验,推动人工智能技术在医疗卫生领域的发展与应用。
  • 表  1  已发表的高质量AI临床试验研究示例

    Table  1.   Examples of published high-quality AI clinical trials

    表  2  SPIRIT-AI条目a

    Table  2.   Items in SPIRIT-AI a

    条目 涉及方案内容 具体条目内容
    SPIRIT-AI 1(ⅰ) 标题 指出干预涉及AIb/机器学习,或指出模型类型
    SPIRIT-AI 1(ⅱ) 指明AI干预的预期用途
    SPIRIT-AI 6a (ⅰ) 背景和合理性 解释AI干预在临床背景下的预期用途,包括其目的和预期受众(如:医务人员、患者及公众)
    SPIRIT-AI 6a (ⅱ) 描述与AI干预相关的现有证据
    SPIRIT-AI 9 方法:研究场所 描述将AI干预整合到试验场所中所需的现场和场外条件
    SPIRIT-AI 10 (ⅰ) 方法:入选标准 说明研究对象的纳入排除标准
    SPIRIT-AI 10 (ⅱ) 说明AI干预所需输入数据的纳入排除标准
    SPIRIT-AI 11a (ⅰ) 方法:干预 说明使用的AI算法版本
    SPIRIT-AI 11a (ⅱ) 说明获取和选择AI干预所需输入数据的程序
    SPIRIT-AI 11a (ⅲ) 说明评估和处理质量差或不可用的AI干预所需输入数据的程序
    SPIRIT-AI 11a (ⅳ) 说明在处理AI干预所需输入数据的过程中是否存在人与AI的交互,以及需要用户达到的专业水平
    SPIRIT-AI 11a (ⅴ) 说明AI干预的输出
    SPIRIT-AI 11a (ⅵ) 解释AI干预的输出如何有助于决策或临床实践的其他要素
    SPIRIT-AI 22 方法:监测可能的错误 说明识别和分析AI干预出现错误的任何计划。如果没有这方面的计划,说明为什么不这样做。
    SPIRIT-AI 29 伦理和传播:代码获取 说明是否可以获取AI干预和/或其代码。如果可以,如何获取(包括对获取或再使用的任何限制条件)。
    注:a标准方案条目:对人工智能干预试验的建议(standard protocol items: recommendations for interventional trials-artificial intelligence, SPIRIT-AI),人工智能(artificial intelligence, AI)。
    下载: 导出CSV
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    [2] Rivera SC, Liu X, Chan AW, et al. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI Extension[J]. BMJ, 2020, 370:m3210. DOI: 10.1136/bmj.m3210.
    [3] Caballero RE, García SG, Rigl AM, et al. A web-based clinical decision support system for gestational diabetes: Automatic diet prescription and detection of insulin needs[J]. Int J Med Inform, 2017, 102:35-49. DOI: 10.1016/j.ijmedinf.2017.02.014.
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    [5] Labovitz DL, Shafner L, Reyes GM, et al. Using artificial intelligence to reduce the risk of nonadherence in patients on anticoagulation therapy[J]. Stroke, 2017, 48(5):1416-1419. DOI: 10.1161/STROKEAHA.116.016281.
    [6] Lin H, Li R, Liu Z, et al. Diagnostic efficacy and therapeutic decision-making capacity of an artificial intelligence platform for childhood cataracts in eye clinics: a multicentre randomized controlled trial[J]. EClinical Medicine, 2019, 9:52-59. DOI: 10.1016/j.eclinm.2019.03.001.
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    [8] Mathew B, Norris D, Hendry D, et al. Artificial intelligence in the diagnosis of low-back pain and sciatica[J]. Spine, 1988, 13(2):168-172. DOI: 10.1097/00007632-198802000-00007.
    [9] Pavel AM, Rennie JM, De Vries LS, et al. A machine-learning algorithm for neonatal seizure recognition: a multicentre, randomised, controlled trial[J]. Lancet Child Adolesc Health, 2020, 4(10):740-749. DOI: 10.1016/s2352-4642(20)30239-x.
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    [12] Wang P, Berzin TM, Glissen Brown JR, et al. Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study[J]. Gut, 2019, 68(10):1813-1819. DOI: 10.1136/gutjnl-2018-317500.
    [13] Persell SD, Peprah YA, Lipiszko D, et al. Effect of home blood pressure monitoring via a smartphone hypertension coaching application or tracking application on adults with uncontrolled hypertension: a randomized clinical trial[J]. JAMA Netw Open, 2020, 3(3):e200255. DOI: 10.1001/jamanetworkopen.2020.0255.
    [14] Chan AW, Tetzlaff JM, Altman DG, et al. SPIRIT 2013 statement: defining standard protocol items for clinical trials[J]. Ann Intern Med, 2013, 158(3):200-207. DOI: 10.7326/0003-4819-158-3-201302050-00583.
    [15] Liu X, Rivera SC, Moher D, et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI Extension[J]. BMJ, 2020, 370:m3164. DOI: 10.1136/bmj.m3164.
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    [17] Washington P, Voss C, Kline A, et al. SuperpowerGlass: a wearable aid for the at-home therapy of children with autism[J]. Proc ACM Interact Mob Wearable Ubiquitous Technol, 2017, 1(3):112. DOI: 10.1145/3130977.
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出版历程
  • 收稿日期:  2020-12-08
  • 修回日期:  2020-12-21
  • 刊出日期:  2021-01-10

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