Hôpital Sainte-Marguerite

Five simultaneous artificial intelligence data challenges on ultrasound, CT, and MRI

Nathalie Lassau, Théo Estienne, P. de Vomecourt et al.

2019 · 43 Zit.

Deep learning for the radiographic diagnosis of proximal femur fractures: Limitations and programming issues

Sylvain Guy, Christophe Jacquet, Damien Tsenkoff et al.

2021 · 28 Zit.

Evaluation of the impact of large language learning models on articles submitted to Orthopaedics & Traumatology: Surgery & Research (OTSR): A significant increase in the use of artificial intelligence in 2023

Gaëlle Maroteau, Jae‐Sung An, Jérôme Murgier et al.

2023 · 24 Zit.

French community grid for the evaluation of radiological artificial intelligence solutions (DRIM France Artificial Intelligence Initiative)

Daphné Guenoun, Marc Zins, Pierre Champsaur et al.

2023 · 17 Zit.

Artificial intelligence for bone fracture detection: A promising tool but no substitute for human expertise

Daphné Guenoun, Mickaël Tordjman

2024 · 4 Zit.

Reply to the letter from Liming Zhu, Guofei Kong and Changhua Liu

Gaëlle Maroteau, Jea-Sung An, C. Hulet et al.

2024 · 0 Zit.

AI in orthopedics: how to measure one’s real contribution and avoid cheating?

Grégoire Micicoi, Matthieu Ollivier, JN Argenson

2026 · 0 Zit.

Évaluation de l’impact des large language learning models sur les articles soumis à Orthopedics & Traumatology: Surgery & Research (OTSR) : une augmentation significative de l’utilisation de l’intelligence artificielle en 2023

Gaëlle Maroteau, Jae‐Sung An, Jérôme Murgier et al.

2023 · 0 Zit.

Mortality associated with the perioperative management of patients with proximal femur fractures: A machine learning prediction model

Thomas A. Rocco, François Antonini, Thibaut Florant et al.

2025 · 0 Zit.