TY - JOUR
T1 - Artificial intelligence for precision oncology
T2 - beyond patient stratification
AU - Azuaje, Francisco
N1 - Publisher Copyright:
© 2019, The Author(s).
PY - 2019/12/1
Y1 - 2019/12/1
N2 - The data-driven identification of disease states and treatment options is a crucial challenge for precision oncology. Artificial intelligence (AI) offers unique opportunities for enhancing such predictive capabilities in the lab and the clinic. AI, including its best-known branch of research, machine learning, has significant potential to enable precision oncology well beyond relatively well-known pattern recognition applications, such as the supervised classification of single-source omics or imaging datasets. This perspective highlights key advances and challenges in that direction. Furthermore, it argues that AI’s scope and depth of research need to be expanded to achieve ground-breaking progress in precision oncology.
AB - The data-driven identification of disease states and treatment options is a crucial challenge for precision oncology. Artificial intelligence (AI) offers unique opportunities for enhancing such predictive capabilities in the lab and the clinic. AI, including its best-known branch of research, machine learning, has significant potential to enable precision oncology well beyond relatively well-known pattern recognition applications, such as the supervised classification of single-source omics or imaging datasets. This perspective highlights key advances and challenges in that direction. Furthermore, it argues that AI’s scope and depth of research need to be expanded to achieve ground-breaking progress in precision oncology.
UR - http://www.scopus.com/inward/record.url?scp=85116953480&partnerID=8YFLogxK
U2 - 10.1038/s41698-019-0078-1
DO - 10.1038/s41698-019-0078-1
M3 - Review article
AN - SCOPUS:85116953480
SN - 2397-768X
VL - 3
JO - npj Precision Oncology
JF - npj Precision Oncology
IS - 1
M1 - 6
ER -