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Challenges Related to Artificial Intelligence Research in Medical Imaging and the Importance of Image Analysis Competitions

2019; Radiological Society of North America; Volume: 1; Issue: 1 Linguagem: Inglês

10.1148/ryai.2019180031

2638-6100

Luciano M. Prevedello, Safwan S. Halabi, George Shih, Carol C. Wu, Marc D. Kohli, Falgun H. Chokshi, Bradley J. Erickson, Jayashree Kalpathy‐Cramer, Katherine P. Andriole, Adam E. Flanders,

Artificial Intelligence in Healthcare and Education

In recent years, there has been enormous interest in applying artificial intelligence (AI) to radiology. Although some of this interest may have been driven by exaggerated expectations that the technology can outperform radiologists in some tasks, there is a growing body of evidence that illustrates its limitations in medical imaging. The true potential of the technique probably lies somewhere in the middle, and AI will ultimately play a key role in medical imaging in the future. The limitless power of computers makes AI an ideal candidate to provide the standardization, consistency, and dependability needed to support radiologists in their mission to provide excellent patient care. However, important roadblocks currently limit the expansion of this field in medical imaging. This article reviews some of the challenges and potential solutions to advance the field forward, with focus on the experience gained by hosting image-based competitions. Keywords: Convolutional Neural Network (CNN), Diagnosis, Supervised learning