Oxford BioMedica (United Kingdom)
Relevante Arbeiten
Meistzitierte Publikationen im Bereich Gesundheit & MedTech
Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies
Myura Nagendran, Yang Chen, Christopher A. Lovejoy et al.
2020 · 990 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.
Deep learning based tissue analysis predicts outcome in colorectal cancer
Dmitrii Bychkov, Nina Linder, Riku Turkki et al.
2018 · 642 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.
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.
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 · 298 Zit.
A quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies: QUADAS-AI
Viknesh Sounderajah, Hutan Ashrafian, Sherri Rose et al.
2021 · 217 Zit.
Explainable artificial intelligence for mental health through transparency and interpretability for understandability
Dan W. Joyce, Andrey Kormilitzin, Katharine Smith et al.
2023 · 191 Zit.
Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review
Paula Dhiman, Jie Ma, Constanza L. Andaur Navarro et al.
2022 · 124 Zit.
Completeness of reporting of clinical prediction models developed using supervised machine learning: a systematic review
Constanza L. Andaur Navarro, Johanna AAG Damen, Toshihiko Takada et al.
2022 · 119 Zit.
Reporting of prognostic clinical prediction models based on machine learning methods in oncology needs to be improved
Paula Dhiman, Jie Ma, Constanza L. Andaur Navarro et al.
2021 · 106 Zit.
Systematic review identifies the design and methodological conduct of studies on machine learning-based prediction models
Constanza L. Andaur Navarro, Johanna AAG Damen, Maarten van Smeden et al.
2022 · 89 Zit.
Establishing key research questions for the implementation of artificial intelligence in colonoscopy: a modified Delphi method
Omer F. Ahmad, Yuichi Mori, Masashi Misawa et al.
2020 · 67 Zit.
Risk of bias of prognostic models developed using machine learning: a systematic review in oncology
Paula Dhiman, Jie Ma, Constanza L. Andaur Navarro et al.
2022 · 61 Zit.
Systematic review finds “spin” practices and poor reporting standards in studies on machine learning-based prediction models
Constanza L. Andaur Navarro, Johanna AAG Damen, Toshihiko Takada et al.
2023 · 56 Zit.