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 · 1.885 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 · 1.798 Zit.
Calibration: the Achilles heel of predictive analytics
On behalf of Topic Group ‘Evaluating diagnostic tests and prediction models’ of the STRATOS initiative, Ben Van Calster, David J. McLernon et al.
2019 · 1.744 Zit.
Bias in data‐driven artificial intelligence systems—An introductory survey
Eirini Ntoutsi, Pavlos Fafalios, Ujwal Gadiraju et al.
2020 · 969 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 · 775 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 · 462 Zit.
From hype to reality: data science enabling personalized medicine
Holger Fröhlich, Rudi Balling, Niko Beerenwinkel et al.
2018 · 440 Zit.
Interpretability of machine learning‐based prediction models in healthcare
Gregor Stiglic, Primoz Kocbek, Nino Fijacko et al.
2020 · 383 Zit.
Metrics reloaded: recommendations for image analysis validation
Lena Maier‐Hein, Annika Reinke, Patrick Godau et al.
2024 · 374 Zit.
Surgical data science – from concepts toward clinical translation
Lena Maier‐Hein, Matthias Eisenmann, Duygu Sarıkaya et al.
2022 · 320 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 · 287 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 · 284 Zit.
Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory and Practice
Jeroen Bertels, Tom Eelbode, Maxim Berman et al.
2019 · 270 Zit.
Predictive analytics in health care: how can we know it works?
Ben Van Calster, Laure Wynants, D. Timmerman et al.
2019 · 212 Zit.
Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation (PRIME): A Checklist
Partho P. Sengupta, Sirish Shrestha, Béatrice Berthon et al.
2020 · 211 Zit.