University Medical Center Utrecht

TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods

Gary S. Collins, Karel G.M. Moons, Paula Dhiman et al.

2024 · 1.829 Zit.

Prediction models for cardiovascular disease risk in the general population: systematic review

Johanna AAG Damen, Lotty Hooft, Ewoud Schuit et al.

2016 · 927 Zit.

Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD Statement

Gary S. Collins, Johannes B. Reitsma, Douglas G. Altman et al.

2015 · 847 Zit.

Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence

Gary S. Collins, Paula Dhiman, Constanza L. Andaur Navarro et al.

2021 · 778 Zit.

Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review

Anne de Hond, Artuur Leeuwenberg, Lotty Hooft et al.

2022 · 472 Zit.

Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

Baptiste Vasey, Myura Nagendran, Bruce Campbell et al.

2022 · 468 Zit.

Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness

Sebastian J. Vollmer, Bilal A. Mateen, Gergő Bohner et al.

2020 · 465 Zit.

Machine Learning and Neurosurgical Outcome Prediction: A Systematic Review

Joeky T. Senders, Patrick Staples, Aditya V. Karhade et al.

2017 · 460 Zit.

Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD Statement

Gary S. Collins, Johannes B. Reitsma, Douglas G. Altman et al.

2015 · 443 Zit.

Who is afraid of black box algorithms? On the epistemological and ethical basis of trust in medical AI

Juan M. Durán, Karin Jongsma

2021 · 436 Zit.

Updating methods improved the performance of a clinical prediction model in new patients

K.J.M. Janssen, Karel G.M. Moons, Cor J. Kalkman et al.

2007 · 379 Zit.

Metrics reloaded: recommendations for image analysis validation

Lena Maier‐Hein, Annika Reinke, Patrick Godau et al.

2024 · 376 Zit.

Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review

Constanza L. Andaur Navarro, Johanna AAG Damen, Toshihiko Takada et al.

2021 · 346 Zit.

The TRIPOD-LLM reporting guideline for studies using large language models

Jack Gallifant, Majid Afshar, Saleem Ameen et al.

2025 · 304 Zit.

Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors

L.G.D. Strohm, Charisma Hehakaya, Erik Ranschaert et al.

2020 · 303 Zit.