TY  - GEN
AD  - Maastricht University Medical Centre - GROW (MAASTRO), Maastricht, The Netherlands
AD  - University of Leicester, Leicester, UK
AD  - Consiglio nazionale delle Ricerche, Instituto di Scienze e Technologie della Cognizione (CNR-IST), Roma, Italy
AD  - TheraPanacea, Paris, France
AD  - School of Engineering, Architecture and Landscape (HEPIA), HES-SO // University of Applied Sciences and Arts Western Switzerland
AD  - Athens University of Economics and Business, Athens, Greece
AD  - CENTAI Institute, Turin, Italy
AD  - CENTAI Institute, Turin, Italy
AD  - Maastricht University, Medical Center+, Maastricht, The Netherlands
AD  - University of Leicester, Leicester, UK
AD  - Gustave Roussy, Cancer Campus, Villejuif, France
AD  - Medical Data Works B.V., Maastricht, The Netherlands
AD  - Maastricht Unviversity, Maastricht, The Netherlands
AD  - University of Leicester, Leicester, UK
AD  - Maastricht University Medical Centre - GROW (MAASTRO), Maastricht, The Netherlands
AD  - Unitrad, Unicancer, Paris, France
AD  - Consiglio Nazionale delle Ricerche, Instituto di Scienze e Technologie della Cognizione (CNR-IST), Roma, Italy
AU  - Liang, Y.
AU  - Rainbird, J.
AU  - Cortellessa, G.
AU  - Balia, M.
AU  - Bologna, Guido
AU  - Koutsopoulos, I.
AU  - Pannison, A.
AU  - Perotti, A.
AU  - Ramaekers, B.
AU  - Rattay, T.
AU  - Rivera, S.
AU  - Romita, A.
AU  - Roumen, C.
AU  - Talbot, C.
AU  - Verhoeven, K.
AU  - Bergeaud, M.
AU  - Fracasso, F.
CY  - Amsterdam, The Netherlands
DA  - 2024-10
DO  - 10.1016/j.ijrobp.2024.07.1451
DO  - DOI
EP  - e661-e662
ID  - 15494
JF  - International Journal of Radiation Oncology*Biology*Physics ; Proceedings of the 2024 ASTRO Annual Meeting, 29 September-2 October 2024, Washington, DC, USA
L1  - https://arodes.hes-so.ch/record/15494/files/Bologna_2024_Physicians_view_explainable_AI.pdf
L2  - https://arodes.hes-so.ch/record/15494/files/Bologna_2024_Physicians_view_explainable_AI.pdf
L4  - https://arodes.hes-so.ch/record/15494/files/Bologna_2024_Physicians_view_explainable_AI.pdf
LA  - eng
LK  - https://arodes.hes-so.ch/record/15494/files/Bologna_2024_Physicians_view_explainable_AI.pdf
PB  - Elsevier
PP  - Amsterdam, The Netherlands
PY  - 2024-10
SN  - 0360-3016
SP  - e661-e662
T1  - Physicians’ views on explainable artificial intelligence models to predict the risk of toxicity following breast radiotherapy
TI  - Physicians’ views on explainable artificial intelligence models to predict the risk of toxicity following breast radiotherapy
UR  - https://arodes.hes-so.ch/record/15494/files/Bologna_2024_Physicians_view_explainable_AI.pdf
VL  - 2024, 120
Y1  - 2024-10
ER  -