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Medical Center Utrecht","{\"openalex\":\"I3018483916\"}","2026-05-14T01:44:08.306004217","2026-05-23T11:38:33.212239218","companyType","COMPANY","foundedDate","headquarters","country","NL","employeeCount","fundingStage","totalFunding","valuation","website","stockSymbol","uscc","crunchbaseUrl","company_I193662353","Utrecht University",[135,136,127],"{\"ror\":\"04pp8hn57\",\"openalex\":\"I193662353\"}","2026-05-14T00:32:47.284430094","2026-06-01T04:23:16.316343987","UNIVERSITY","Utrecht, The Netherlands","https://www.uu.nl","Rijksuniversiteit Utrecht","Universiteit Utrecht","company_I136199984","Harvard University",[138,144],"{\"ror\":\"03vek6s52\",\"openalex\":\"I136199984\",\"wikidata\":\"Q13371\"}","2026-05-14T00:28:16.321983458","2026-06-01T04:10:36.334209505","US","Universidad de Harvard","company_I4210116895","Art Innovation (Netherlands)",[146],"{\"ror\":\"02g21hc40\",\"openalex\":\"I4210116895\"}","2026-05-14T16:43:40.180376186","2026-05-21T02:01:31.317520514","Hengelo, 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TRIPOD-LLM reporting guideline for studies using large language models","{\"openalex\":\"W4406152263\",\"doi\":\"10.1038/s41591-024-03425-5\"}","2026-05-21T02:01:19.217188908","2026-05-21T02:01:19.217112513","doi","10.1038/s41591-024-03425-5","arxivId","openalexId","W4406152263","abstractText","publishedDate","2025-01-01","publishedYear",2025,"venue","Nature Medicine",307,"authors","keywords",[187,188,189],"pdfUrl","https://pmc.ncbi.nlm.nih.gov/articles/PMC12104976/pdf/nihms-2079076.pdf","topVenue","Radiomics and Machine Learning in Medical Imaging","Artificial Intelligence in Healthcare and Education","Machine Learning in Healthcare","paper_W2079776874","Updating methods improved the performance of a clinical prediction model in new patients","{\"openalex\":\"W2079776874\",\"doi\":\"10.1016/j.jclinepi.2007.04.018\"}","2026-05-19T20:34:36.196235814","2026-05-19T20:34:36.198992602","10.1016/j.jclinepi.2007.04.018","W2079776874","2007-12-01",2007,"Journal of Clinical Epidemiology",380,[189,202,188],"Sepsis Diagnosis and Treatment","paper_W3011403448","Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness","{\"openalex\":\"W3011403448\",\"doi\":\"10.1136/bmj.l6927\"}","2026-05-16T10:16:52.452000724","2026-05-19T05:04:04.431502482","10.1136/bmj.l6927","W3011403448","Machine learning, artificial intelligence, and other modern statistical methods are providing new opportunities to operationalise previously untapped and rapidly growing sources of data for patient benefit. Despite much promising research currently being undertaken, particularly in imaging, the literature as a whole lacks transparency, clear reporting to facilitate replicability, exploration for potential ethical concerns, and clear demonstrations of effectiveness. Among the many reasons why these problems exist, one of the most important (for which we provide a preliminary solution here) is the current lack of best practice guidance specific to machine learning and artificial intelligence. However, we believe that interdisciplinary groups pursuing research and impact projects involving machine learning and artificial intelligence for health would benefit from explicitly addressing a series of questions concerning transparency, reproducibility, ethics, and effectiveness (TREE). The 20 critical questions proposed here provide a framework for research groups to inform the design, conduct, and reporting; for editors and peer reviewers to evaluate contributions to the literature; and for patients, clinicians and policy makers to critically appraise where new findings may deliver patient benefit.","2020-03-20",2020,"BMJ",465,[188,216,189],"Explainable Artificial Intelligence (XAI)","paper_W4280572991","Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI","{\"openalex\":\"W4280572991\",\"doi\":\"10.1038/s41591-022-01772-9\"}","2026-05-16T09:36:04.230222986","2026-05-19T04:47:21.187509829","10.1038/s41591-022-01772-9","W4280572991","2022-05-01",2022,468,[188,216,189],"https://pure.amsterdamumc.nl/ws/files/139852420/Reporting-guideline-for-the-early-stage-clinical-evaluation-of-decision-support-systems-driven-by-artificial-intelligenc.pdf","paper_W4205805845","Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review","{\"openalex\":\"W4205805845\",\"doi\":\"10.1038/s41746-021-00549-7\"}","2026-05-16T08:43:06.246808912","2026-05-19T04:25:27.494316879","10.1038/s41746-021-00549-7","W4205805845","While the opportunities of ML and AI in healthcare are promising, the growth of complex data-driven prediction models requires careful quality and applicability assessment before they are applied and disseminated in daily practice. This scoping review aimed to identify actionable guidance for those closely involved in AI-based prediction model (AIPM) development, evaluation and implementation including software engineers, data scientists, and healthcare professionals and to identify potential gaps in this guidance. We performed a scoping review of the relevant literature providing guidance or quality criteria regarding the development, evaluation, and implementation of AIPMs using a comprehensive multi-stage screening strategy. PubMed, Web of Science, and the ACM Digital Library were searched, and AI experts were consulted. Topics were extracted from the identified literature and summarized across the six phases at the core of this review: (1) data preparation, (2) AIPM development, (3) AIPM validation, (4) software development, (5) AIPM impact assessment, and (6) AIPM implementation into daily healthcare practice. From 2683 unique hits, 72 relevant guidance documents were identified. Substantial guidance was found for data preparation, AIPM development and AIPM validation (phases 1-3), while later phases clearly have received less attention (software development, impact assessment and implementation) in the scientific literature. The six phases of the AIPM development, evaluation and implementation cycle provide a framework for responsible introduction of AI-based prediction models in healthcare. Additional domain and technology specific research may be necessary and more practical experience with implementing AIPMs is needed to support further guidance.","2022-01-10","npj Digital Medicine",472,[188,243,216],"https://www.nature.com/articles/s41746-021-00549-7.pdf",true,"Artificial Intelligence in Healthcare","paper_W2474667434","External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges","{\"openalex\":\"W2474667434\",\"doi\":\"10.1136/bmj.i3140\"}","2026-05-15T19:34:33.413090539","2026-05-21T14:22:34.188040834","10.1136/bmj.i3140","W2474667434","Access to big datasets from e-health records and individual participant data (IPD) meta-analysis is signalling a new advent of external validation studies for clinical prediction models. In this article, the authors illustrate novel opportunities for external validation in big, combined datasets, while drawing attention to methodological challenges and reporting issues.","2016-06-22",2016,550,[189,257,258],"https://www.bmj.com/content/bmj/353/bmj.i3140.full.pdf","Meta-analysis and systematic reviews","Blood Pressure and Hypertension Studies","paper_W1997192661","A new framework to enhance the interpretation of external validation studies of clinical prediction models","{\"openalex\":\"W1997192661\",\"doi\":\"10.1016/j.jclinepi.2014.06.018\"}","2026-05-15T19:27:08.224969297","2026-05-21T14:07:39.195094079","10.1016/j.jclinepi.2014.06.018","W1997192661","OBJECTIVES: It is widely acknowledged that the performance of diagnostic and prognostic prediction models should be assessed in external validation studies with independent data from \"different but related\" samples as compared with that of the development sample. We developed a framework of methodological steps and statistical methods for analyzing and enhancing the interpretation of results from external validation studies of prediction models. STUDY DESIGN AND SETTING: We propose to quantify the degree of relatedness between development and validation samples on a scale ranging from reproducibility to transportability by evaluating their corresponding case-mix differences. We subsequently assess the models' performance in the validation sample and interpret the performance in view of the case-mix differences. Finally, we may adjust the model to the validation setting. RESULTS: We illustrate this three-step framework with a prediction model for diagnosing deep venous thrombosis using three validation samples with varying case mix. While one external validation sample merely assessed the model's reproducibility, two other samples rather assessed model transportability. The performance in all validation samples was adequate, and the model did not require extensive updating to correct for miscalibration or poor fit to the validation settings. CONCLUSION: The proposed framework enhances the interpretation of findings at external validation of prediction models.","2014-08-30",2014,551,[272,273,274],"http://www.jclinepi.com/article/S0895435614002753/pdf","Venous Thromboembolism Diagnosis and Management","Imbalanced Data Classification Techniques","Blood properties and coagulation","paper_W2024887237","Validation, updating and impact of clinical prediction rules: A review","{\"openalex\":\"W2024887237\",\"doi\":\"10.1016/j.jclinepi.2008.04.008\"}","2026-05-15T13:45:55.401168415","2026-05-21T09:54:00.189128386","10.1016/j.jclinepi.2008.04.008","W2024887237","2008-09-30",2008,592,[286,189,202],"Clinical Reasoning and Diagnostic Skills","paper_W2151056789","Risk prediction models: II. External validation, model updating, and impact assessment","{\"openalex\":\"W2151056789\",\"doi\":\"10.1136/heartjnl-2011-301247\"}","2026-05-14T10:13:11.191471622","2026-05-21T13:52:01.212595447","10.1136/heartjnl-2011-301247","W2151056789","Clinical prediction models are increasingly used to complement clinical reasoning and decision-making in modern medicine, in general, and in the cardiovascular domain, in particular. To these ends, developed models first and foremost need to provide accurate and (internally and externally) validated estimates of probabilities of specific health conditions or outcomes in the targeted individuals. Subsequently, the adoption of such models by professionals must guide their decision-making, and improve patient outcomes and the cost-effectiveness of care. In the first paper of this series of two companion papers, issues relating to prediction model development, their internal validation, and estimating the added value of a new (bio)marker to existing predictors were discussed. In this second paper, an overview is provided of the consecutive steps for the assessment of the model's predictive performance in new individuals (external validation studies), how to adjust or update existing models to local circumstances or with new predictors, and how to investigate the impact of the uptake of prediction models on clinical decision-making and patient outcomes (impact studies). Each step is illustrated with empirical examples from the cardiovascular field.","2012-03-07",2012,"Heart",1180,[300,301,189],"Health Systems, Economic Evaluations, Quality of Life","Healthcare cost, quality, practices","paper_W4233026002","Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement","{\"openalex\":\"W4233026002\",\"doi\":\"10.1136/bmj.g7594\"}","2026-05-14T01:57:26.303765706","2026-05-21T03:40:43.182366895","10.1136/bmj.g7594","W4233026002","Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web based survey and revised during a three day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org). To encourage dissemination of the TRIPOD Statement, this article is freely accessible on the Annals of Internal Medicine Web site (www.annals.org) and will be also published in BJOG, British Journal of Cancer, British Journal of Surgery, BMC Medicine, The BMJ, Circulation, Diabetic Medicine, European Journal of Clinical Investigation, European Urology, and Journal of Clinical Epidemiology. The authors jointly hold the copyright of this article. 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Karel G. M. Moons 人物

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