Erasmus MC

Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints

Tjeerd van der Ploeg, Peter C. Austin, Ewout W. Steyerberg

2014 · 762 Zit.

The role of explainability in creating trustworthy artificial intelligence for health care: A comprehensive survey of the terminology, design choices, and evaluation strategies

Aniek F. Markus, Jan A. Kors, Peter R. Rijnbeek

2020 · 703 Zit.

Natural Language Processing in Radiology: A Systematic Review

Ewoud Pons, Loes Braun, M. G. Myriam Hunink et al.

2016 · 558 Zit.

Why rankings of biomedical image analysis competitions should be interpreted with care

Lena Maier‐Hein, Matthias Eisenmann, Annika Reinke et al.

2018 · 343 Zit.

Moving from bytes to bedside: a systematic review on the use of artificial intelligence in the intensive care unit

Davy van de Sande, Michel E. van Genderen, Joost Huiskens et al.

2021 · 247 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.

Integrating artificial intelligence into the clinical practice of radiology: challenges and recommendations

Michael P. Recht, Marc Dewey, Keith Dreyer et al.

2020 · 229 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 · 218 Zit.

Critical appraisal of artificial intelligence-based prediction models for cardiovascular disease

Maarten van Smeden, Georg Heinze, Ben Van Calster et al.

2022 · 129 Zit.

Developing, implementing and governing artificial intelligence in medicine: a step-by-step approach to prevent an artificial intelligence winter

Davy van de Sande, Michel E. van Genderen, Jim M Smit et al.

2022 · 118 Zit.

Steps to avoid overuse and misuse of machine learning in clinical research

Victor Volovici, Nicholas Syn, Ari Ercole et al.

2022 · 104 Zit.

Computational pathology in 2030: a Delphi study forecasting the role of AI in pathology within the next decade

M. Álvaro Berbís, David S. McClintock, Andrey Bychkov et al.

2023 · 97 Zit.

Trustworthy AI: Closing the gap between development and integration of AI systems in ophthalmic practice

Cristina González-Gonzalo, Eric F. Thee, Caroline C. W. Klaver et al.

2021 · 92 Zit.

Big science and big data in nephrology

Julio Sáez-Rodríguez, Markus M. Rinschen, Jürgen Floege et al.

2019 · 80 Zit.

Machine learning in neurosurgery: a global survey

Victor E. Staartjes, Vittorio Stumpo, Julius M. Kernbach et al.

2020 · 79 Zit.