Erasmus MC

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 · 738 Zit.

Natural Language Processing in Radiology: A Systematic Review

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

2016 · 571 Zit.

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

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

2018 · 361 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 · 304 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 · 264 Zit.

Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): an international, paired, non-inferiority, confirmatory study

Anindo Saha, Joeran Sander Bosma, Jasper J. Twilt et al.

2024 · 259 Zit.

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

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

2020 · 237 Zit.

Design and implementation of a standardized framework to generate and evaluate patient-level prediction models using observational healthcare data

Jenna Reps, Martijn J. Schuemie, Marc A. Suchard et al.

2018 · 231 Zit.

Ethical Issues of Digital Twins for Personalized Health Care Service: Preliminary Mapping Study

Pei‐hua Huang, Ki-Hun Kim, Maartje Schermer

2021 · 138 Zit.

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

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

2022 · 131 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 · 122 Zit.

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

Victor Volovici, Nicholas Syn, Ari Ercole et al.

2022 · 108 Zit.

Evidence-based radiology: why and how?

Francesco Sardanelli, M. G. Myriam Hunink, Fiona J. Gilbert et al.

2009 · 106 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 · 101 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 · 96 Zit.