Hôpital Européen Georges-Pompidou
Relevante Arbeiten
Meistzitierte Publikationen im Bereich Gesundheit & MedTech
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.
METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII
Burak Koçak, Tugba Akinci D’Antonoli, Nathaniel D. Mercaldo et al.
2024 · 255 Zit.
A Chatbot Versus Physicians to Provide Information for Patients With Breast Cancer: Blind, Randomized Controlled Noninferiority Trial
Jean‐Emmanuel Bibault, Benjamin Chaix, Arthur Guillemassé et al.
2019 · 198 Zit.
AI-driven quantification, staging and outcome prediction of COVID-19 pneumonia
Guillaume Chassagnon, Maria Vakalopoulou, Enzo Battistella et al.
2020 · 158 Zit.
Artificial intelligence in diagnostic and interventional radiology: Where are we now?
Tom Boeken, Jean Feydy, Augustin Lecler et al.
2022 · 135 Zit.
Natural language processing of radiology reports for the detection of thromboembolic diseases and clinically relevant incidental findings
Anne-Dominique Pham, Aurélie Névéol, Thomas Lavergne et al.
2014 · 108 Zit.
A framework for validating AI in precision medicine: considerations from the European ITFoC consortium
Rosy Tsopra, Xosé M. Fernández, Claudio Luchinat et al.
2021 · 100 Zit.
Radiomics: A primer for the radiation oncologist
J.-E. Bibault, L. Xing, Philippe Giraud et al.
2020 · 77 Zit.
Clinical Trial Design Principles and Outcomes Definitions for Device-Based Therapies for Hypertension: A Consensus Document From the Hypertension Academic Research Consortium
David E. Kandzari, Felix Mahfoud, Michael A. Weber et al.
2022 · 72 Zit.
A joint ESTRO and AAPM guideline for development, clinical validation and reporting of artificial intelligence models in radiation therapy
Coen Hurkmans, Jean‐Emmanuel Bibault, Kristy K. Brock et al.
2024 · 68 Zit.
Assessment of Performance, Interpretability, and Explainability in Artificial Intelligence–Based Health Technologies: What Healthcare Stakeholders Need to Know
Line Farah, Juliette Murris, Isabelle Borget et al.
2023 · 63 Zit.
Dynamic prediction of renal survival among deeply phenotyped kidney transplant recipients using artificial intelligence: an observational, international, multicohort study
Marc Raynaud, Olivier Aubert, Gillian Divard et al.
2021 · 63 Zit.
Application of Machine Learning Models to Predict Recurrence After Surgical Resection of Nonmetastatic Renal Cell Carcinoma
Z. Khene, Pierre Bigot, N. Doumerc et al.
2022 · 49 Zit.
Five simultaneous artificial intelligence data challenges on ultrasound, CT, and MRI
Nathalie Lassau, Théo Estienne, P. de Vomecourt et al.
2019 · 44 Zit.
Three artificial intelligence data challenges based on CT and MRI
Nathalie Lassau, Imad Bousaid, Émilie Chouzenoux et al.
2020 · 43 Zit.