[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125817-en":3,"doc-seo-125817-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125817,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","TRIPOD+AI statement - updated guidance for reporting clinical prediction models that use regression or machine learning methods","TRIPOD+AI provides harmonised, transparent reporting guidance for clinical prediction model studies that use regression modelling or machine learning. The update addresses ongoing concerns about incomplete and insufficiently transparent reporting that limit critical appraisal, reduce confidence in findings, and hinder usability, evaluation, and implementation. TRIPOD+AI introduces a superseding 27-item checklist with expanded explanations and a dedicated for Abstracts checklist, aiming to support accurate study review by researchers, clinicians, editors, policymakers, end users, and patients.","RESEARCH METHODS AND REPORTING  \nFor numbered affiliations see end of the article  \nCorrespondence to: G S Collins [gary.collins@csm.ox.ac. uk](gary.collins@csm.ox.ac. uk)[ ](gary.collins@csm.ox.ac. uk)(or @GSCollins on Twitter; ORCID 0000-0002-2772-2316) Additional material is published online only. To view please visit the journal online.  \nCite thisas: BMJ2024;385:e078378  \n[http://dx.doi.org/10.1136/](http://dx.doi.org/10.1136/)[ ](http://dx.doi.org/10.1136/)bmj-2023-078378  \nAccepted: 17 January 2024  \nTRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods  \nGary S Collins, 1 Karel G M Moons,2 Paula Dhiman, 1 Richard D Riley,3,4 Andrew L Beam, 5 Ben Van Calster,6,7 Marzyeh Ghassemi,8 Xiaoxuan Liu,9,10 Johannes B Reitsma,2 Maarten van Smeden,2 Anne-Laure Boulesteix, 11 Jennifer Catherine Camaradou, 12,13 Leo Anthony Celi, 14,15,16 Spiros Denaxas, 17,18 Alastair K Denniston,4,9 Ben Glocker, 19 Robert M Golub,20 Hugh Harvey,21 Georg Heinze,22 Michael M Hoffman,23,24,25,26  \nAndré Pascal Kengne,27 Emily Lam, 12 Naomi Lee,28 Elizabeth W Loder,29,30 Lena Maier-Hein,31 Bilal A Mateen, 17,32,33 Melissa D McCradden,34,35 Lauren Oakden-Rayner,36 Johan Ordish,37 Richard Parnell, 12 Sherri Rose,38 Karandeep Singh,39 Laure Wynants,40 Patricia Logullo 1  \nThe TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) statement was published in 2015 to provide the minimum reporting recommendations for studies developing or evaluating the performance of a prediction model. Methodological advances in the field of prediction have since included the widespread use of artificial intelligence (AI) powered by machine learning methods to develop prediction models. An update to the TRIPOD statement is thus needed. TRIPOD+AI provides harmonised guidance for reporting prediction model studies, irrespective  \nof whether regression modelling or machine learning methods have been used. The new checklist supersedes the TRIPOD 2015 checklist, which should no longer be used. This article describes the development of TRIPOD+AI and presents the expanded 27 item checklist with more detailed explanation of each reporting recommendation, and the TRIPOD+AI for Abstracts checklist. TRIPOD+AI aims to promote the complete, accurate, and transparent reporting of studies that develop a prediction model or evaluate its performance. Complete reporting will facilitate study appraisal, model evaluation, and model implementation.  \nSUMMARY POINTS  \nThere has been considerable interest and financial investment in developing prediction models by applying artificial intelligence (AI) methods, typically powered by advances in machine learning  \nTo ensure that a prediction model study is valuable to users, authors should prepare a transparent, complete, and accurate account of why the research was done, what they did, and what they found  \nAn update of the TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) statement aims to harmonise the landscape of prediction model studies using AI methods and to provide guidance regardless of whether regression models or machine learning methods have been used  \nThe TRIPOD+AI statement consists of a 27 item checklist, an expanded checklist that details reporting recommendations for each item, and a TRIPOD+AI for Abstracts checklist containing 13 items  \nTRIPOD+AI aims to assist authors in the complete reporting of their study and help peer reviewers, editors, policymakers, end users, and patients understand the data, methods, findings and conclusions of AI driven research  \nAdherence to the TRIPOD+AI reporting recommendations could encourage the improved use of research time, effort, and money  \nPrediction models are used across different healthcare settings. They are used to estimate an outcome value or risk. Most models estimate the probability of the presence of a p","cbCaivE1mb8gVN9P","https://ap.wps.com/l/cbCaivE1mb8gVN9P","pdf",291263,1,14,"English","en",105,"# Summary points\n## Purpose and rationale\n## Core model concepts and clinical use\n## TRIPOD+AI structure and checklist coverage\n## Guidance impact on appraisal and implementation","[{\"question\":\"What problem does TRIPOD+AI address in prediction model research?\",\"answer\":\"It addresses longstanding concerns about incomplete and insufficiently transparent reporting that impair critical appraisal, confidence in results, and the ability to evaluate or implement prediction models.\"},{\"question\":\"What does TRIPOD+AI cover for reporting clinical prediction model studies?\",\"answer\":\"It provides harmonised guidance for studies developing or evaluating prediction models, regardless of whether regression or machine learning methods were used, including expanded explanations and an abstracts checklist.\"},{\"question\":\"What has changed compared with the original TRIPOD 2015 statement?\",\"answer\":\"TRIPOD+AI is an updated guidance that supersedes the 2015 checklist; the new 27-item checklist should no longer be replaced by the older version.\"}]","TRIPOD+AI statement - updated guidance for reporting clinical prediction models that use regression or machine learning methods | PDF",1785901378,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"tripodai-statement-updated-guidance-for-reporting-clinical-prediction-models-that-use-regression-or-machine-learning-methods","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/tripodai-statement-updated-guidance-for-reporting-clinical-prediction-models-that-use-regression-or-machine-learning-methods/125817/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does TRIPOD+AI address in prediction model research?","Question",{"text":75,"@type":76},"It addresses longstanding concerns about incomplete and insufficiently transparent reporting that impair critical appraisal, confidence in results, and the ability to evaluate or implement prediction models.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What does TRIPOD+AI cover for reporting clinical prediction model studies?",{"text":80,"@type":76},"It provides harmonised guidance for studies developing or evaluating prediction models, regardless of whether regression or machine learning methods were used, including expanded explanations and an abstracts checklist.",{"name":82,"@type":73,"acceptedAnswer":83},"What has changed compared with the original TRIPOD 2015 statement?",{"text":84,"@type":76},"TRIPOD+AI is an updated guidance that supersedes the 2015 checklist; 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