KU Leuven
Relevante Arbeiten
Meistzitierte Publikationen im Bereich Gesundheit & MedTech
A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models
Evangelia Christodoulou, Jie Ma, Gary S. Collins et al.
2019 · 1.818 Zit.
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.
Bias in data‐driven artificial intelligence systems—An introductory survey
Eirini Ntoutsi, Pavlos Fafalios, Ujwal Gadiraju et al.
2020 · 944 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.
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.
Interpretability of machine learning‐based prediction models in healthcare
Gregor Stiglic, Primoz Kocbek, Nino Fijacko et al.
2020 · 372 Zit.
Metrics reloaded: recommendations for image analysis validation
Lena Maier‐Hein, Annika Reinke, Patrick Godau et al.
2024 · 338 Zit.
Surgical data science – from concepts toward clinical translation
Lena Maier‐Hein, Matthias Eisenmann, Duygu Sarıkaya et al.
2022 · 311 Zit.
Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory and Practice
Jeroen Bertels, Tom Eelbode, Maxim Berman et al.
2019 · 262 Zit.
FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare
Karim Lekadir, Alejandro F. Frangi, Antonio R. Porras et al.
2025 · 219 Zit.
Predictive analytics in health care: how can we know it works?
Ben Van Calster, Laure Wynants, D. Timmerman et al.
2019 · 209 Zit.
Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation (PRIME): A Checklist
Partho P. Sengupta, Sirish Shrestha, Béatrice Berthon et al.
2020 · 205 Zit.
PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods
Karel G.M. Moons, Johanna AAG Damen, T. K. Kaul et al.
2025 · 179 Zit.
Understanding metric-related pitfalls in image analysis validation
Annika Reinke, Minu D. Tizabi, Michael Baumgartner et al.
2024 · 154 Zit.
How the EU Can Achieve Legally Trustworthy AI: A Response to the European Commission’s Proposal for an Artificial Intelligence Act
Nathalie A. Smuha, Emma Ahmed-Rengers, Adam Harkens et al.
2021 · 146 Zit.