Much of the published literature in Radiology-related Artificial Intelligence(AI)focuses on single tasks,such as identifying the presence or absence or severity of specific lesions.Progress comparable to that achieved...Much of the published literature in Radiology-related Artificial Intelligence(AI)focuses on single tasks,such as identifying the presence or absence or severity of specific lesions.Progress comparable to that achieved for general-purpose computer vision has been hampered by the unavailability of large and diverse radiology datasets containing different types of lesions with possibly multiple kinds of abnormalities in the same image.Also,since a diagnosis is rarely achieved through an image alone,radiology AI must be able to employ diverse strategies that consider all available evidence,not just imaging information.Using key imaging and clinical signs will help improve their accuracy and utility tremendously.Employing strategies that consider all available evidence will be a formidable task;we believe that the combination of human and computer intelligence will be superior to either one alone.Further,unless an AI application is explainable,radiologists will not trust it to be either reliable or bias-free;we discuss some approaches aimed at providing better explanations,as well as regulatory concerns regarding explainability(“transparency”).Finally,we look at federated learning,which allows pooling data from multiple locales while maintaining data privacy to create more generalizable and reliable models,and quantum computing,still prototypical but potentially revolutionary in its computing impact.展开更多
We suggest an augmentation of the excellent comprehensive review article titled“Comprehensive literature review on the radiographic findings,imaging modalities,and the role of radiology in the coronavirus disease 201...We suggest an augmentation of the excellent comprehensive review article titled“Comprehensive literature review on the radiographic findings,imaging modalities,and the role of radiology in the coronavirus disease 2019(COVID-19)pandemic”under the following categories:(1)“Inclusion of additional radiological features,related to pulmonary infarcts and to COVID-19 pneumonia”;(2)“Amplified discussion of cardiovascular COVID-19 manifestations and the role of cardiac magnetic resonance imaging in monitoring and prognosis”;(3)“Imaging findings related to fluorodeoxyglucose positron emission tomography,optical,thermal and other imaging modalities/devices,including‘intelligent edge’and other remote monitoring devices”;(4)“Artificial intelligence in COVID-19 imaging”;(5)“Additional annotations to the radiological images in the manuscript to illustrate the additional signs discussed”;and(6)“A minor correction to a passage on pulmonary destruction”.展开更多
文摘Much of the published literature in Radiology-related Artificial Intelligence(AI)focuses on single tasks,such as identifying the presence or absence or severity of specific lesions.Progress comparable to that achieved for general-purpose computer vision has been hampered by the unavailability of large and diverse radiology datasets containing different types of lesions with possibly multiple kinds of abnormalities in the same image.Also,since a diagnosis is rarely achieved through an image alone,radiology AI must be able to employ diverse strategies that consider all available evidence,not just imaging information.Using key imaging and clinical signs will help improve their accuracy and utility tremendously.Employing strategies that consider all available evidence will be a formidable task;we believe that the combination of human and computer intelligence will be superior to either one alone.Further,unless an AI application is explainable,radiologists will not trust it to be either reliable or bias-free;we discuss some approaches aimed at providing better explanations,as well as regulatory concerns regarding explainability(“transparency”).Finally,we look at federated learning,which allows pooling data from multiple locales while maintaining data privacy to create more generalizable and reliable models,and quantum computing,still prototypical but potentially revolutionary in its computing impact.
文摘We suggest an augmentation of the excellent comprehensive review article titled“Comprehensive literature review on the radiographic findings,imaging modalities,and the role of radiology in the coronavirus disease 2019(COVID-19)pandemic”under the following categories:(1)“Inclusion of additional radiological features,related to pulmonary infarcts and to COVID-19 pneumonia”;(2)“Amplified discussion of cardiovascular COVID-19 manifestations and the role of cardiac magnetic resonance imaging in monitoring and prognosis”;(3)“Imaging findings related to fluorodeoxyglucose positron emission tomography,optical,thermal and other imaging modalities/devices,including‘intelligent edge’and other remote monitoring devices”;(4)“Artificial intelligence in COVID-19 imaging”;(5)“Additional annotations to the radiological images in the manuscript to illustrate the additional signs discussed”;and(6)“A minor correction to a passage on pulmonary destruction”.
