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BibTex format

@article{Koh:2022:10.1038/s43856-022-00199-0,
author = {Koh, D-M and Papanikolaou, N and Bick, U and Illing, R and Kahn, CE and Kalpathi-Cramer, J and Matos, C and Martí-Bonmatí, L and Miles, A and Mun, SK and Napel, S and Rockall, A and Sala, E and Strickland, N and Prior, F},
doi = {10.1038/s43856-022-00199-0},
journal = {Commun Med (Lond)},
title = {Artificial intelligence and machine learning in cancer imaging.},
url = {http://dx.doi.org/10.1038/s43856-022-00199-0},
volume = {2},
year = {2022}
}

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TY  - JOUR
AB  - An increasing array of tools is being developed using artificial intelligence (AI) and machine learning (ML) for cancer imaging. The development of an optimal tool requires multidisciplinary engagement to ensure that the appropriate use case is met, as well as to undertake robust development and testing prior to its adoption into healthcare systems. This multidisciplinary review highlights key developments in the field. We discuss the challenges and opportunities of AI and ML in cancer imaging; considerations for the development of algorithms into tools that can be widely used and disseminated; and the development of the ecosystem needed to promote growth of AI and ML in cancer imaging.
AU  - Koh,D-M
AU  - Papanikolaou,N
AU  - Bick,U
AU  - Illing,R
AU  - Kahn,CE
AU  - Kalpathi-Cramer,J
AU  - Matos,C
AU  - Martí-Bonmatí,L
AU  - Miles,A
AU  - Mun,SK
AU  - Napel,S
AU  - Rockall,A
AU  - Sala,E
AU  - Strickland,N
AU  - Prior,F
DO  - 10.1038/s43856-022-00199-0
PY  - 2022///
TI  - Artificial intelligence and machine learning in cancer imaging.
T2  - Commun Med (Lond)
UR  - http://dx.doi.org/10.1038/s43856-022-00199-0
UR  - https://www.ncbi.nlm.nih.gov/pubmed/36310650
VL  - 2
ER  -

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