TY - JOUR
T1 - Beyond the Artificial Intelligence Hype
T2 - What Lies Behind the Algorithms and What We Can Achieve
AU - Van Assen, Marly
AU - Banerjee, Imon
AU - De Cecco, Carlo N.
N1 - Publisher Copyright:
© 2020 Lippincott Williams and Wilkins. All rights reserved.
PY - 2020/5/1
Y1 - 2020/5/1
N2 - The field of artificial intelligence (AI) is currently experiencing a period of extensive growth in a wide variety of fields, medicine not being the exception. The base of AI is mathematics and computer science, and the current fame of AI in industry and research stands on 3 pillars: big data, high performance computing infrastructure, and algorithms. In the current digital era, increased storage capabilities and data collection systems, lead to a massive influx of data for AI algorithm. The size and quality of data are 2 major factors influencing performance of AI applications. However, it is highly dependent on the type of task at hand and algorithm chosen to perform this task. AI may potentially automate several tedious tasks in radiology, particularly in cardiothoracic imaging, by pre-readings for the detection of abnormalities, accurate quantifications, for example, oncologic volume lesion tracking and cardiac volume and image optimization. Although AI-based applications offer great opportunity to improve radiology workflow, several challenges need to be addressed starting from image standardization, sophisticated algorithm development, and large-scale evaluation. Integration of AI into the clinical workflow also needs to address legal barriers related to security and protection of patient-sensitive data and liability before AI will reach its full potential in cardiothoracic imaging.
AB - The field of artificial intelligence (AI) is currently experiencing a period of extensive growth in a wide variety of fields, medicine not being the exception. The base of AI is mathematics and computer science, and the current fame of AI in industry and research stands on 3 pillars: big data, high performance computing infrastructure, and algorithms. In the current digital era, increased storage capabilities and data collection systems, lead to a massive influx of data for AI algorithm. The size and quality of data are 2 major factors influencing performance of AI applications. However, it is highly dependent on the type of task at hand and algorithm chosen to perform this task. AI may potentially automate several tedious tasks in radiology, particularly in cardiothoracic imaging, by pre-readings for the detection of abnormalities, accurate quantifications, for example, oncologic volume lesion tracking and cardiac volume and image optimization. Although AI-based applications offer great opportunity to improve radiology workflow, several challenges need to be addressed starting from image standardization, sophisticated algorithm development, and large-scale evaluation. Integration of AI into the clinical workflow also needs to address legal barriers related to security and protection of patient-sensitive data and liability before AI will reach its full potential in cardiothoracic imaging.
KW - artificial intelligence
KW - cardiac imaging
KW - radiology
KW - thoracic imaging
UR - http://www.scopus.com/inward/record.url?scp=85083912447&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85083912447&partnerID=8YFLogxK
U2 - 10.1097/RTI.0000000000000485
DO - 10.1097/RTI.0000000000000485
M3 - Review article
C2 - 32073539
AN - SCOPUS:85083912447
SN - 0883-5993
VL - 35
SP - S3-S10
JO - Journal of thoracic imaging
JF - Journal of thoracic imaging
ER -