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 · 1.438 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 · 736 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 · 461 Zit.

Machine Learning and Neurosurgical Outcome Prediction: A Systematic Review

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

2017 · 450 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 · 449 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 · 428 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 · 416 Zit.

Metrics reloaded: recommendations for image analysis validation

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

2024 · 338 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 · 332 Zit.

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

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

2020 · 291 Zit.

Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol

Viknesh Sounderajah, Hutan Ashrafian, Robert Golub et al.

2021 · 283 Zit.

Natural and Artificial Intelligence in Neurosurgery: A Systematic Review

Joeky T. Senders, Omar Arnaout, Aditya V. Karhade et al.

2017 · 254 Zit.

Sharing ICU Patient Data Responsibly Under the Society of Critical Care Medicine/European Society of Intensive Care Medicine Joint Data Science Collaboration: The Amsterdam University Medical Centers Database (AmsterdamUMCdb) Example*

Patrick Thoral, Jan M. Peppink, Ronald H. Driessen et al.

2021 · 242 Zit.

Missing data is poorly handled and reported in prediction model studies using machine learning: a literature review

Steven W J Nijman, Artuur Leeuwenberg, Inés Beekers et al.

2021 · 227 Zit.

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

Jack Gallifant, Majid Afshar, Saleem Ameen et al.

2025 · 226 Zit.