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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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Tuberculosis screening among children and adolescents in China:insights from a mathematical model

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Intelligent Medicine 2023年第3期3卷 157-163页

作者： janne estill Yangqin Xu Shouyuan Wu Lidong Hu Nan Yang Shu Yang Yaolong Chen Guobao Li Evidence-Based Medicine Center School of Basic Medical SciencesLanzhou UniversityLanzhouGansu 730000China Institute of Global Health University of GenevaGenevaSwitzerland School of Public Health Lanzhou UniversityLanzhouGansu 730000China Department of Rheumatology and Immunology The First Medical CenterChinese PLA General HospitalBejing 100853China College of Medical Information Engineering Chengdu University of Traditional Chinese MedicineChengduSichuan 610000China Research Unit of Evidence-Based Evaluation and Guidelines Chinese Academy of Medical Sciences(2021RU017)School of Basic Medical SciencesLanzhou UniversityLanzhouGansu 730000China Lanzhou University Institute of Health Data Science LanzhouGansu 730000China WHO Collaborating Centre for Guideline Implementation and Knowledge Translation LanzhouGansu 730000China Department of Lung Disease Shenzhen Third People's HospitalThe Second Affiliated HospitalSouthern University of Science and TechnologyShenzhen Guangdong 518112China

Background Tuberculosis(TB)continues to be prevalent in China also among children and adolescents in China.We built a dynamic mathematical model for TB transmission in China,and applied it to compare the epidemic trends 2021–2030 under a range of screening interventions focusing on children and adolescents.Methods We developed a dynamic mathematical model with a flexible structure.The model can be applied either stochastically or deterministically,and can encompass arbitrary age structure and resistance levels.In the present version,we used the deterministic version excluding resistance but including age structure with six groups:0–5,6–11,12–14,15–17,18–64,and 65 years and above.We parameterized the model by literature data and fitting it to case and death estimates provided by the World Health Organization.We compared the new TB cases and TB-related deaths in each age group over the period 2021–2030 in 10 scenarios that involved intensified screening of particular age groups of children,adolescents,or young adults,or decreased or increased diagnostic accuracy of the screening.Results Screening the entire age class of 18-year-old persons would prevent 517,000 TB cases and 14,600 TB-related deaths between years 2021 and 2030,corresponding to 6.6%and 5.5%decrease from the standard of care projection,respectively.Annual screening of children aged 6–11 and,to a lesser extent,0–5 years,also reduced TB incidence and mortality,particularly among children of the respective ages but also in other age groups.In contrast,intensified screening of adolescents did not have a major impact.Screening with a simpler and less accurate method resulted in worsened outcomes,which could not be offset by more intensive screening.More accurate screening and better sensitivity to detect latent TB could prevent 2.3 million TB cases and 68,500 TB deaths in the coming 10 years.Conclusion Routine screening in schools can efficiently reduce the burden of TB in China.Screening should be intensified particularly among children in primary school

关键词： Tuberculosis China Children and adolescents Mathematical model Screening

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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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Development of the Scientific,Transparent and Applicable Rankings (STAR) tool for clinical practice guidelines

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Chinese Medical Journal 2023年第12期136卷 1430-1438页

作者： Nan Yang Hui Liu Wei Zhao Yang Pan Xiangzheng Lyu Xiuyuan Hao Xiaoqing Liu Wen’an Qi Tong Chen Xiaoqin Wang Boheng Zhang Weishe Zhang Qiu Li Dong Xu Xinghua Gao Yinghui Jin Feng Sun Wenbo Meng Guobao Li Qijun Wu Ze Chen Xu Wang janne estill Susan LNorris Liang Du Yaolong Chen Junmin Wei Evidence-Based Medicine Center School of Basic Medical SciencesLanzhou UniversityLanzhouGansu 730000China Institute of Global Health University of GenevaGeneva 1202Switzerland School of Public Health Lanzhou UniversityLanzhouGansu 730000China Editor-in-Chief Office Chinese Medical Association Publishing HouseBeijing 100052China Marketing Department Chinese Medical Association Publishing HouseBeijing 100052China Editorial Department National Medical Journal of ChinaChinese Medical Association Publishing HouseBeijing 100052China Editorial Department Chinese Medical JournalChinese Medical Association Publishing HouseBeijing 100052China Division of Infectious Diseases Department of Internal MedicineState Key Laboratory of Complex Severe and Rare DiseasePeking Union Medical College HospitalChinese Academy of Medical Sciences&Peking Union Medical CollegeBeijing 100730China Editorial office of Digital Medicine and Health Chinese Medical Association Publishing HouseBeijing 100052China Department of Hematology Huashan HospitalFudan UniversityShanghai 200040China Evidence-based Medicine Center Fudan UniversityShanghai 200032China Department of Hepatic Oncology Xiamen Clinical Research Center for Cancer TherapyZhongshan Hospital(Xiamen)Fudan UniversityXiamenFujian 361015China Department of Obstetrics Xiangya HospitalCentral South UniversityChangshaHunan 410008China Department of Nephrology Children’s Hospital of Chongqing Medical UniversityNational Clinical Research Center for Child Health and DisordersMinistry of Education Key Laboratory of Child Development and DisordersChongqing Key Laboratory of PediatricsChongqing 400014China SMU Institute for Global Health(SIGHT) School of Health Management and Dermatology HospitalSouthern Medical University(SMU)GuangzhouGuangdong 510515China Department of Dermatology The First Hospital of China Medical UniversityShenyangLiaoning 110001China Center for Evidence-Based and Translational Medicine Zhongnan Hospital of Wuhan UniversityWu

Background:This study aimed to develop a comprehensive instrument for evaluating and ranking clinical practice guidelines,named Scientific,Transparent and Applicable Rankings tool(STAR),and test its reliability,validity,and usability.Methods:This study set up a multidisciplinary working group including guideline methodologists,statisticians,journal editors,clinicians,and other experts.Scoping review,Delphi methods,and hierarchical analysis were used to develop the STAR tool.We evaluated the instrument’s intrinsic and interrater reliability,content and criterion validity,and usability.Results:STAR contained 39 items grouped into 11 domains.The mean intrinsic reliability of the domains,indicated by Cronbach’sαcoefficient,was 0.588(95%confidence interval[CI]:0.414,0.762).Interrater reliability as assessed with Cohen’s kappa coefficient was 0.774(95%CI:0.740,0.807)for methodological evaluators and 0.618(95%CI:0.587,0.648)for clinical evaluators.The overall content validity index was 0.905.Pearson’s r correlation for criterion validity was 0.885(95%CI:0.804,0.932).The mean usability score of the items was 4.6 and the median time spent to evaluate each guideline was 20 min.Conclusion:The instrument performed well in terms of reliability,validity,and efficiency,and can be used for comprehensively evaluating and ranking guidelines.

关键词： Practice guideline Evidence-based practice Quality control

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Analysis of COVID-19 Guideline Quality and Change of Recommendations:A Systematic Review

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Health Data Science 2021年第1期2021卷 88-109页

作者： Siya Zhao Shuya Lu Shouyuan Wu Zijun Wang Qiangqiang Guo Qianling Shi Hairong Zhang Juanjuan Zhang Hui Liu Yunlan Liu Xianzhuo Zhang Ling Wang Mengjuan Ren Ping Wang Hui Lan Qi Zhou Yajia Sun Jin Cao Qinyuan Li janne estill Joseph LMathew Hyeong Sik Ahn Myeong Soo Lee Xiaohui Wang Chenyan Zhou Yaolong Chen School of Public Health Lanzhou UniversityLanzhouChina Institute of Health Data Science Lanzhou UniversityLanzhouChina Department of Pediatric Sichuan Provincial People’s HospitalUniversity of Electronic Science and Technology of ChinaChengduChina Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital ChengduChina Evidence-Based Medicine Center School of Basic Medical SciencesLanzhou UniversityLanzhouChina The First School of Clinical Medicine Lanzhou UniversityLanzhouChina National Clinical Research Center for Child Health and Disorders Ministry of Education Key Laboratory of Child Development and DisordersChina International Science and Technology Cooperation Base of Child Development and Critical DisordersChildren’s Hospital of Chongqing Medical UniversityChongqingChina Institute of Global Health University of GenevaGenevaSwitzerland Institute of Mathematical Statistics and Actuarial Science University of BernBernSwitzerland Advanced Pediatrics Centre PGIMER ChandigarhChandigarhIndia Department of Preventive Medicine Korea UniversitySeoulRepublic of Korea Korea Cochrane Centre SeoulRepublic of Korea Evidence Based Medicine SeoulRepublic of Korea Korea University School of Medicine SeoulRepublic of Korea Korea Institute of Oriental Medicine DaejeonRepublic of Korea University of Science and Technology DaejeonRepublic of Korea London Southbank University LondonUK Tianjin University of Traditional Chinese Medicine TianjinChina WHO Collaborating Centre for Guideline Implementation and Knowledge Translation LanzhouChina Guideline International Network Asia China Key Laboratory of Evidence Based Medicine and Knowledge Translation of Gansu Province Lanzhou UniversityLanzhouChina Lanzhou University GRADE Center China

Background.Hundreds of coronavirus disease 2019(COVID-19)clinical practice guidelines(CPGs)and expert consensus statements have been developed and published since the outbreak of the epidemic.However,these CPGs are of widely variable quality.So,this review is aimed at systematically evaluating the methodological and reporting qualities of COVID-19 CPGs,exploring factors that may influence their quality,and analyzing the change of recommendations in CPGs with evidence published.Methods.We searched five electronic databases and five websites from 1 January to 31 December 2020 to retrieve all COVID-19 CPGs.The assessment of the methodological and reporting qualities of CPGs was performed using the AGREE II instrument and RIGHT checklist.Recommendations and evidence used to make recommendations in the CPGs regarding some treatments for COVID-19(remdesivir,glucocorticoids,hydroxychloroquine/chloroquine,interferon,and lopinavir-ritonavir)were also systematically assessed.And the statistical inference was performed to identify factors associated with the quality of CPGs.Results.We included a total of 92 COVID-19 CPGs developed by 19 countries.Overall,the RIGHT checklist reporting rate of COVID-19 CPGs was 33.0%,and the AGREE II domain score was 30.4%.The overall methodological and reporting qualities of COVID-19 CPGs gradually improved during the year 2020.Factors associated with high methodological and reporting qualities included the evidence-based development process,management of conflicts of interest,and use of established rating systems to assess the quality of evidence and strength of recommendations.The recommendations of only seven(7.6%)CPGs were informed by a systematic review of evidence,and these seven CPGs have relatively high methodological and reporting qualities,in which six of them fully meet the Institute of Medicine(IOM)criteria of guidelines.Besides,a rapid advice CPG developed by the World Health Organization(WHO)of the seven CPGs got the highest overall scores in methodological(72.8%)and reporting

关键词： register formal transparency

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