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Application of artificial intelligence in clinical diagnosis and treatment:an overview of systematic reviews

Intelligent Medicine 2022年第2期2卷 88-96页

作者： Shouyuan Wu Jianjian Wang Qiangqiang Guo Hui Lan Juanjuan Zhang Ling Wang Estill Janne Xufei Luo Qi Wang Yang Song Joseph LMathew Yangqin Xun Nan Yang Myeong Soo Lee Yaolong Chen School of Public health Lanzhou UniversityLanzhouGansu 730000China Institute of Global health University of GenevaGenevaSwitzerland Institute of Mathematical Statistics and Actuarial Science University of BernBernSwitzerland Department of health Research Methods Evidence and ImpactFaculty of Health SciencesMcMaster UniversityHamiltonCanada McMaster Health Forum McMaster UniversityHamiltonCanada Iberoamerican Cochrane Centre-Biomedical Research Institute Sant Pau(IIB Sant Pau) BarcelonaSpain Advanced Pediatrics Centre PGIMER ChandigarhChandigarhIndia Evidence-Based Medicine Center School of Basic Medical SciencesLanzhouGansu 730000China Korea Institute of Oriental Medicine DaejeonKorea University of Science and Technology DaejeonKorea London Southbank University LondonUK Tianjin University of Traditional Chinese Medicine Tianjin 301617China Research Unit of Evidence-Based Evaluation and Guidelines Chinese Academy of Medical Sciences(2021RU0170)School of Basic Medical SciencesLanzhou UniversityLanzhouGansu 730000China Lanzhou University Institute of health Data Science LanzhouGansu 730000China Key Laboratory of Evidence Based Medicine&Knowledge Translation of Gansu Province LanzhouGansu 730000China

Objective This study aimed to summarize the characteristics and methodological quality of systematic reviews on the application of artificial intelligence(AI)in clinical diagnosis and treatment.Methods We systematically searched seven English-and Chinese-language literature databases to identify sys-tematic reviews on the application of AI,deep learning,or machine learning in the diagnosis and treatment of any disease published in 2020.We evaluated the methodological quality of the included systematic reviews using“A Measurement tool for the assessment of multiple systematic reviews”(AMSTAR).We also conducted meta-analyses on the diagnostic accuracy of AI on selected disease categories with a large number of included studies and low clinical heterogeneity.Results A total of 40 systematic reviews reporting 1,083 original studies were included,covering 31 diseases from 11 groups of diseases.Eleven systematic reviews were related to neoplasms and nine were systematic reviews related to diseases of the digestive system.We selected digestive system diseases for the meta-analysis.The pooled sensitivities(with 95%confidence interval(CI))of AI to assist the diagnosis of helicobacter pylori,gastrointestinal ulcers,hemorrhage,esophageal tumors,gastric tumors,and intestinal tumors(with 95%CI)were 0.91(0.83-0.95),0.99(0.76-1.00),0.95(0.83-0.99),0.90(0.85-0.93),0.90(0.82-0.95),and 0.93(0.88-0.96),respectively,and the pooled specificities were 0.82(0.77-0.87),0.97(0.86-1.00),1.00(0.99-1.00),0.80(0.71-0.87),0.93(0.87-0.97),and 0.89(0.85-0.92),respectively.The AMSTAR items“the list of included studies”(n=39,97.5%)and“the characteristics of the included studies”(n=39,97.5%)had the highest compliance among the reviews;the compliance was relatively low to the items“the consideration of publication status”(n=1,2.5%),“the consideration of scientific quality”(n=19,47.5%),“data synthesis methods”(n=18,45.0%),and“the evaluation of publication bias”(n=13,32.5%).Conclusions The main subjects of systematic reviews on AI applications in cli

关键词： Artificial intelligence Overview of systematic reviews Diagnosis Treatment

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卫生系统知证政策指导：联系指导制定和政策制定

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中国循证医学杂志 2014年第7期14卷 783-788页

作者： John N. Lavis John-Arne Rottingen Xavier Bosch-Capblanch Rifat Atun Fadi E1-Jardali Lucy Gilson Simon Lewin Sandy Oliver Pierre Ongolo-Zogo Andy Haines 吴琼芳(译) 王小琴(译) 陈耀龙(审校) 杨克虎(审校) McMaster Health Forum Centre for Health Economics and Policy AnalysisDepartment of Clinical Epidemiology and Biostatisticsand Department of Political ScienceMcMaster UniversityHamiltonOntarioCanada Norwegian Knowledge Centre for the health Services OsloNorway Swiss Tropical and Public health Institute BaselSwitzerland Global Fund to Fight AIDS Tuberculosis and MalariaGenevaSwitzerland and Imperial CollegeLondonUnited Kingdom Department of health Policy and Management American University of BeirutBeirutLebanon and McMaster Health ForumMcMaster UniversityHamiltonOntarioCanada University of Cape Town Cape TownSouth Africaand London School of Hygiene and Tropical MedicineLondonUnited Kingdom Norwegian Knowledge Centre for the health Services OsloNorwayand Health Systems Research UnitMedical Research Council of South AfricaCape TownSouth Africa EPPI-Centre Social Science Research UnitInstitute of EducationLondonUnited Kingdom Centre for the Development of Best Practices in health Yaounde"Central Hospitaland University of Yaounde"Yaounde"Cameroon London School of Hygiene and Tropical Medicine LondonUnited Kingdom 兰州大学循证医学中心/中同GRADE中心兰州大学基础医学院兰州730000 甘肃省循证医学与临床转化重点实验室兰州730000

本文是《PLoS Medicine》关于卫生系统知证政策指导三篇系列文章的第二篇,阐述了应如何将证据转化为指导,以形成卫生系统政策,并促进临床和公共卫生干预的实施。其要点是:①背景因素在卫生系统决策中极其重要,政策制定者在采用具体卫生... 详细信息

本文是《PLoS Medicine》关于卫生系统知证政策指导三篇系列文章的第二篇,阐述了应如何将证据转化为指导,以形成卫生系统政策,并促进临床和公共卫生干预的实施。其要点是:①背景因素在卫生系统决策中极其重要,政策制定者在采用具体卫生系统指导前,必须全面考虑不同方案的利弊;②全球指导制定者和政策制定者、国家指导制定者和政策制定者有必要分工协作以支持卫生系统知证决策;③负责在全球层面制定卫生系统指导的小组可以通过确保其成果能用于全球层面和国家层面的政策制定以及国家层面的指导制定,来发挥其最大作用;④指导和政策的制定需要严格的全球和国家卫生系统分析和政策系统分析的支持;⑤指导制定和政策制定的分工以及支持卫生系统和政策分析的框架有待进一步研究。

关键词：国家卫生系统政策指导分工协作卫生干预背景因素

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卫生系统知证政策指导：评估对研究证据的信心

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中国循证医学杂志 2014年第7期14卷 789-794页

作者： Simon Lewin Xavier Bosch-Capblanch Sandy Oliver Elie A.Akl Gunn E.Vist John N.Lavis Davina Ghersi John-Arne Rottingen Peter Steinmann Metin Gulmezoglu Peter Tugwell Fadi ElJardali Andy Haines 王小琴(译) 王琪(译) 陈耀龙(审校) 杨克虎(审校) Norwegian Knowledge Centre for the health Services OsloNorwayand Health Systems Research UnitMedical Research Council of South AfricaCape TownSouth Africa Swiss Tropical and Public health Institute Baseland University of BaselBaselSwitzerland EPPI-Centre Social Science Research UnitInstitute of EducationLondonUnited Kingdom Department of Medicine State University of New York at BuffaloBuffaloNew YorkUnited States of Americaand Department of Clinical Epidemiology and BiostatisticsMcMaster UniversityHamiltonOntarioCanada Norwegian Knowledge Centre for the health Services OsloNorway McMaster Health Forum Centre for Health Economics and Policy AnalysisDepartment of Clinical Epidemiology and Biostatisticsand Department of Political ScienceMcMaster UniversityHamiltonOntarioCanada Department of Research Policy and Cooperation Informationl Evidence and ResearchWorld Health OrganizationGenevaSwitzerland Norwegian Knowledge Centre for the health Services OsloNorway Swiss Tropical and Public health Institute BaselSwitzerland UNDP/UNFPA/WHO/World Bank Special Programmer of Research Development and Research Training in Human ReproductionWorld Health OrganizationSwitzerland Department of Medicine Institute of Population Healthand Department of Epidemiology and Community MedicineUniversity of OttawaOttawaCanada Department of health Policy and Management American University of BeirutBeirutLebanonand McMaster Health ForumMcMaster UniversityHamiltonOntarioCanada Departments of Social and Environmental health Research and of Nutrition and Public health Research London School of Hygiene and Tropical MedicineLondonUnited Kingdom 兰州大学循证医学中心/中国GRADE中心兰州大学基础医学院兰州730000 甘肃省循证医学与临床转化重点实验室兰州730000

本文是《PLoS Medicine》关于卫生系统知证政策指导三篇系列文章的第三篇,阐述了应如何将证据转化为指导,以形成卫生系统政策,并促进临床和公共卫生干预的实施。其要点如下:①评估不同类型研究证据是决定解决卫生系统问题的政策方案的关... 详细信息

本文是《PLoS Medicine》关于卫生系统知证政策指导三篇系列文章的第三篇,阐述了应如何将证据转化为指导,以形成卫生系统政策,并促进临床和公共卫生干预的实施。其要点如下:①评估不同类型研究证据是决定解决卫生系统问题的政策方案的关键;②鉴于许多卫生系统干预的复杂性,系统透明的评价方法尤为重要;③当前有不少好的工具可用于评价对支持政策制定各阶段不同类型的研究证据的信心,其中评价关于效果的证据最多;④我们需要制定工具来帮助评价其他关键因素的系统评价证据,如政策方案对于利益相关者的可接受性、实施可行性及公平性;⑤还需要进一步研究全球卫生系统指导政策方案的制定、构建及呈现方法。

关键词：卫生系统评价证据政策指导信心评估利益相关者 PLoS 卫生干预

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Investigation and evaluation of randomized controlled trials for interventions involving artificial intelligence

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Intelligent Medicine 2021年第2期1卷 61-69页

作者： Jianjian Wang Shouyuan Wu Qiangqiang Guo Hui Lan Estill Janne Ling Wang Juanjuan Zhang Qi Wang Yang Song Nan Yang Xufei Luo Qi Zhou Qianling Shi Xuan Yu Yanfang Ma Joseph LMathew Hyeong Sik Ahn Myeong Soo Lee Yaolong Chen School of Public health Lanzhou UniversityLanzhouGansu 730000China Institute of Global health University of GenevaGenevaSwitzerland Institute of Mathematical Statistics and Actuarial Science University of BernBernSwitzerland Department of health Research Methods Evidence and ImpactFaculty of Health SciencesMcMaster UniversityHamiltonCanada McMaster Health Forum McMaster UniversityHamiltonCanada Iberoamerican Cochrane Centre-Biomedical Research Institute Sant Pau(IIB Sant Pau) BarcelonaSpain Evidence-based Medicine Center School of Basic Medical SciencesLanzhou UniversityLanzhouGansu 730000China The First School of Clinical Medicine Lanzhou UniversityLanzhouGansu 730000China Advanced Pediatrics Centre PGIMER ChandigarhChandigarhIndia Department of Preventive Medicine Korea UniversitySeoulKorea Korea Cochrane Centre SeoulKorea Evidence Based Medicine SeoulKorea Korea University School of Medicine SeoulKorea Korea Institute of Oriental Medicine DaejeonKorea University of Science and Technology DaejeonKorea London Southbank University LondonUK Tianjin University of Traditional Chinese Medicine Tianjin 301617China Lanzhou University Institute of health Data Science LanzhouGansu 730000China Key Laboratory of Evidence Based Medicine&Knowledge Translation of Gansu Province LanzhouGansu 730000China

Objective Complete and transparent reporting is of critical importance for randomized controlled trials(RCTs).The present study aimed to determine the reporting quality and methodological quality of RCTs for interventions involving artificial intelligence(AI)and their protocols.Methods We searched MEDLINE(via PubMed),Embase,Web of Science,CBMdisc,Wanfang Data,and CNKI from January 1,2016,to November 11,2020,to collect RCTs involving AI.We also extracted the protocol of each included RCT if it could be obtained.CONSORT-AI(Consolidated Standards of Reporting Trials-Artificial Intelligence)statement and Cochrane Collaboration’s tool for assessing risk of bias(ROB)were used to evaluate the reporting quality and methodological quality,respectively,and SPIRIT-AI(The Standard Protocol Items:Recommendations for Interventional Trials-Artificial Intelligence)statement was used to evaluate the reporting quality of the protocols.The associations of the reporting rate of CONSORT-AI with the publication year,journal’s impact factor(IF),number of authors,sample size,and first author’s country were analyzed univariately using Pearson’s chi-squared test,or Fisher’s exact test if the expected values in any of the cells were below 5.The compliance of the retrieved protocols to SPIRIT-AI was presented descriptively.Results Overall,29 RCTs and three protocols were considered eligible.The CONSORT-AI items“title and abstract”and“interpretation of results”were reported by all RCTs,with the items with the lowest reporting rates being“funding”(0),“implementation”(3.5%),and“harms”(3.5%).The risk of bias was high in 13(44.8%)RCTs and not clear in 15(51.7%)RCTs.Only one RCT(3.5%)had a low risk of bias.The compliance was not significantly different in terms of the publication year,journal’s IF,number of authors,sample size,or first author’s country.Ten of the 35 SPIRIT-AI items(funding,participant timeline,allocation concealment mechanism,implementation,data management,auditing,declaration of interests,access to data,informed consent material

关键词： Artificial intelligence Randomized controlled trials Reporting quality Methodological quality

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卫生系统知证政策指导：指导制定的基本原理和挑战

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中国循证医学杂志 2014年第7期14卷 777-782页

作者： Xavier Bosch-Capblanch John N. Lavis Simon Lewin Rifat Atun John-Arne Rottingen Daniel Droesche Lise Beck Edgardo Abalos Fadi El-Jardali Lucy Gilson Sandy Oliver Kaspar Wyss Peter Tugwell Regina Kulier Tikki Pang Andy Haines 王琪(译) 吴琼芳(译) 陈耀龙(审校) 杨克虎(审校) Swiss Tropical and Public health Institute BaselSwitzerland University of Basel BaselSwitzerland McMaster Health Forum Centre for Health Economics and Policy AnalysisDepartment of Clinical Epidemiology and Biostatisticsand Department of Political ScienceMcMaster UniversityHamiltonOntarioCanada Norwegian Knowledge Centre for the health Services OsloNorwayand Health Systems Research UnitMedical Research Council of South AfricaCape TownSouth Africa Global Fund to Fight AIDS Tuberculosis and MalariaGenevaSwitzerland Harvard Kennedy School CambridgeMassachusettsUnited States of America Centro Rosarino de Estudios Perinatales RosarioArgentina Department of health Policy and Management American University of BeirutBeirutLebanonand McMaster Health ForumMcMaster UniversityHamiltonOntarioCanada School of Public health University of Cape Town and Department of Global Health and DevelopmentLondon School of Hygiene and Tropical MedicineUnited Kingdom EPPI-Centre Social Science Research UnitInstitute of EducationLondonUnited Kingdom Centre for Global health Institute of Population HealthUniversity of OttawaOttawaOntarioCanada Innovation InformationEvidence and ResearchWorld Health OrganizationGenevaSwitzerland Departments of Social and Environmental health Research and of Nutrition and Public health Research London School of Hygiene&Tropical MedicineLondonUnited Kingdom 兰州大学循证医学中心/中国GRADE中心兰州大学基础医学院兰州730000 甘肃省循证医学与临床转化重点实验室兰州730000

本文是《PLoS Medicine》关于卫生系统知证政策指导三篇系列文章的第一篇,阐述了应如何将证据转化为指导,以形成卫生系统政策,并促进临床和公共卫生干预的实施。其要点如下:①薄弱的卫生系统阻碍了有效干预的实施,加强此系统的政策需要... 详细信息

本文是《PLoS Medicine》关于卫生系统知证政策指导三篇系列文章的第一篇,阐述了应如何将证据转化为指导,以形成卫生系统政策,并促进临床和公共卫生干预的实施。其要点如下:①薄弱的卫生系统阻碍了有效干预的实施,加强此系统的政策需要基于当前可得的最佳证据;②卫生系统证据最好以政策制定过程参考的指导的形式呈现,但目前卫生系统指导制定质量较差;③将问题、干预及实施相关的研究转化为影响卫生系统组织方式的决策和政策是卫生系统指导制定面临的一个挑战;④制定及时且适用于广大卫生系统利益相关者的指导,并确定评价卫生系统指导质量的方法是另一个挑战;⑤改编现有方法(如用于临床指南的那些方法)制定有意义的建议来应对卫生系统、政策系统及背景的复杂性,尚需进一步研究。

关键词：卫生系统政策指导卫生干预最佳证据临床指南利益相关者 PLoS 制定过程

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