Potential of E-Learning Interventions and Artificial Intelligence-Assisted Contouring Skills in Radiotherapy: The ELAISA Study
Journal
JCO Global Oncology
Date Issued
2024-09-05
Author(s)
Rasmussen, Mathis Ersted
Akbarov, Kamal
Titovich, Egor
Nijkamp, Jasper Albertus
Van Elmpt, Wouter
Primdahl, Hanne
Lassen, Pernille
Cacicedo, Jon
Cordero-Mendez, Lisbeth
Uddin, A F M Kamal
Mohamed, Ahmed
Prajogi, Ben
Brohet, Kartika Erida
Nyongesa, Catherine
Lomidze, Darejan
Prasiko, Gisupnikha
Ferraris, Gustavo
Mahmood, Humera
Isayev, Isa
Mohamad, Issa
Shirley, Leivon
Kochbati, Lotfi
Eftodiev, Ludmila
Piatkevich, Maksim
Bonilla Jara, Maria Matilde
Spahiu, Orges
Aralbayev, Rakhat
Zakirova, Raushan
Subramaniam, Sandya
Kibudde, Solomon
Tsegmed, Uranchimeg
Korreman, Stine Sofia
Eriksen, Jesper Grau
DOI
10.1200/GO.24.00173
Abstract
Most research on artificial intelligence-based auto-contouring as template (AI-assisted contouring) for organs-at-risk (OARs) stem from high-income countries. The effect and safety are, however, likely to depend on local factors. This study aimed to investigate the effects of AI-assisted contouring and teaching on contouring time and contour quality among radiation oncologists (ROs) working in low- and middle-income countries (LMICs).
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