TY - JOUR
T1 - Integrating artificial intelligence into the clinical practice of radiology
T2 - challenges and recommendations
AU - Recht, Michael P.
AU - Dewey, Marc
AU - Dreyer, Keith
AU - Langlotz, Curtis
AU - Niessen, Wiro
AU - Prainsack, Barbara
AU - Smith, John J.
PY - 2020
Y1 - 2020
N2 - Abstract: Artificial intelligence (AI) has the potential to significantly disrupt the way radiology will be practiced in the near future, but several issues need to be resolved before AI can be widely implemented in daily practice. These include the role of the different stakeholders in the development of AI for imaging, the ethical development and use of AI in healthcare, the appropriate validation of each developed AI algorithm, the development of effective data sharing mechanisms, regulatory hurdles for the clearance of AI algorithms, and the development of AI educational resources for both practicing radiologists and radiology trainees. This paper details these issues and presents possible solutions based on discussions held at the 2019 meeting of the International Society for Strategic Studies in Radiology. Key Points: • Radiologists should be aware of the different types of bias commonly encountered in AI studies, and understand their possible effects. • Methods for effective data sharing to train, validate, and test AI algorithms need to be developed. • It is essential for all radiologists to gain an understanding of the basic principles, potentials, and limits of AI.
AB - Abstract: Artificial intelligence (AI) has the potential to significantly disrupt the way radiology will be practiced in the near future, but several issues need to be resolved before AI can be widely implemented in daily practice. These include the role of the different stakeholders in the development of AI for imaging, the ethical development and use of AI in healthcare, the appropriate validation of each developed AI algorithm, the development of effective data sharing mechanisms, regulatory hurdles for the clearance of AI algorithms, and the development of AI educational resources for both practicing radiologists and radiology trainees. This paper details these issues and presents possible solutions based on discussions held at the 2019 meeting of the International Society for Strategic Studies in Radiology. Key Points: • Radiologists should be aware of the different types of bias commonly encountered in AI studies, and understand their possible effects. • Methods for effective data sharing to train, validate, and test AI algorithms need to be developed. • It is essential for all radiologists to gain an understanding of the basic principles, potentials, and limits of AI.
KW - Artificial intelligence
KW - Bioethics
KW - Data
KW - Education
KW - Regulation
UR - http://www.scopus.com/inward/record.url?scp=85079714896&partnerID=8YFLogxK
U2 - 10.1007/s00330-020-06672-5
DO - 10.1007/s00330-020-06672-5
M3 - Article
C2 - 32064565
AN - SCOPUS:85079714896
SN - 0938-7994
VL - 30
SP - 3576
EP - 3584
JO - European Radiology
JF - European Radiology
IS - 6
ER -