TY - JOUR
T1 - Interpretable artificial intelligence in radiology and radiation oncology
AU - Cui, Sunan
AU - Traverso, Alberto
AU - Niraula, Dipesh
AU - Zou, Jiaren
AU - Luo, Yi
AU - Owen, Dawn
AU - El Naqa, Issam
AU - Wei, Lise
PY - 2023/10/1
Y1 - 2023/10/1
N2 - Artificial intelligence has been introduced to clinical practice, especially radiology and radiation oncology, from image segmentation, diagnosis, treatment planning and prognosis. It is not only crucial to have an accurate artificial intelligence model, but also to understand the internal logic and gain the trust of the experts. This review is intended to provide some insights into core concepts of the interpretability, the state-of-the-art methods for understanding the machine learning models, the evaluation of these methods, identifying some challenges and limits of them, and gives some examples of medical applications.
AB - Artificial intelligence has been introduced to clinical practice, especially radiology and radiation oncology, from image segmentation, diagnosis, treatment planning and prognosis. It is not only crucial to have an accurate artificial intelligence model, but also to understand the internal logic and gain the trust of the experts. This review is intended to provide some insights into core concepts of the interpretability, the state-of-the-art methods for understanding the machine learning models, the evaluation of these methods, identifying some challenges and limits of them, and gives some examples of medical applications.
U2 - 10.1259/bjr.20230142
DO - 10.1259/bjr.20230142
M3 - (Systematic) Review article
SN - 0007-1285
VL - 96
JO - British Journal of Radiology
JF - British Journal of Radiology
IS - 1150
M1 - 20230142
ER -