Sherry Huang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Sherry Huang has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Ali Gholamrezanezhad
Department of Radiology, Keck School of Medicine, University of Southern California (USC), Los Angeles, USA
1 shared publication - Arnav Rashid
Department of Biological Sciences, Dana and David Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, CA 90089, USA
1 shared publication - Bino Varghese
Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
1 shared publication - Kishan Patel
Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
1 shared publication
Latest Publications
- Open Access
A Narrative Review of the Use of Artificial Intelligence in Breast, Lung, and Prostate Cancer
Artificial intelligence (AI) has been an important topic within radiology. Currently, AI is used clinically to assist with the detection of lesions through detection systems. However, a number of recent studies have demonstrated the increased value of neural networks in radiology. With an increasing number of screening requirements for cancers, this review aims to study the accuracy of the numerous AI models used in the detection and diagnosis of breast, lung, and prostate cancers. This study summarizes pertinent findings from reviewed articles and provides analysis on the relevancy to clinical radiology. This study found that whereas AI is showing continual improvement in radiology, AI alone does not surpass the effectiveness of a radiologist. Additionally, it was found that there are multiple variations on how AI should be integrated with a radiologist’s workflow.
Open AccessA Narrative Review of the Use of Artificial Intelligence in Breast, Lung, and Prostate Cancer
Artificial intelligence (AI) has been an important topic within radiology. Currently, AI is used clinically to assist with the detection of lesions through detection systems. However, a number of recent studies have demonstrated the increased value of neural networks in radiology. With an increasing number of screening requirements for cancers, this review aims to study the accuracy of the numerous AI models used in the detection and diagnosis of breast, lung, and prostate cancers. This study summarizes pertinent findings from reviewed articles and provides analysis on the relevancy to clinical radiology. This study found that whereas AI is showing continual improvement in radiology, AI alone does not surpass the effectiveness of a radiologist. Additionally, it was found that there are multiple variations on how AI should be integrated with a radiologist’s workflow.