The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Challenges Related to Artificial Intelligence Research in Medical Imaging and the Importance of Image Analysis Competitions
Open accessAbstract
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.
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Cites
The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository
Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., et al. (2013) · Journal of Digital Imaging · doi:10.1007/s10278-013-9622-7 · Cited by 4,599
Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T., Kendall, C. B., Gotway, M. B., & Liang, J. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2535302 · Cited by 3,277
Rajpurkar, P., Irvin, J., Ball, R. L., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C. P., et al. (2018) · PLOS Medicine · doi:10.1371/journal.pmed.1002686 · Cited by 1,449
Self-citationYan, K., Wang, X., Lu, L., & Summers, R. M. (2018) · Journal of medical imaging · doi:10.1117/1.jmi.5.3.036501 · Cited by 573
Larson, D. B., Chen, M. C., Lungren, M. P., Halabi, S. S., Stence, N. V., & Langlotz, C. P. (2018) · Radiology · doi:10.1148/radiol.2017170236 · Cited by 456
Self-citationDeep Learning is Robust to Massive Label Noise
Rolnick, D., Veit, A., Belongie, S., & Shavit, N. (2017) · arXiv · doi:10.48550/arxiv.1705.10694 · Cited by 402
Automated Segmentation of Hippocampal Subfields in Drug-Naïve Patients with Alzheimer Disease
Lim, H. K., Hong, S. C., Jung, W. S., Ahn, K. J., Won, W. Y., Hahn, C., Kim, I. S., & Lee, C. U. (2013) · American Journal of Neuroradiology · doi:10.3174/ajnr.a3293 · Cited by 52
Cited by
Orlando, J. I., Fu, H., Barbosa Breda, J., van Keer, K., Bathula, D. R., Diaz-Pinto, A., Fang, R., Heng, P.-A., Kim, J., Lee, J., et al. (2020) · Medical Image Analysis · doi:10.1016/j.media.2019.101570 · Cited by 855
Causality matters in medical imaging
Castro, D. C., Walker, I., & Glocker, B. (2020) · Nature Communications · doi:10.1038/s41467-020-17478-w · Cited by 364
Ethical considerations for artificial intelligence: an overview of the current radiology landscape.
Department of Radiology and Nuclear Medicine, U. H. B., & Akinci D'Antonoli, T. (2020) · Diagnostic and Interventional Radiology · doi:10.5152/dir.2020.19279 · Cited by 80
Choudhury, A. (2022) · JMIR Human Factors · doi:10.2196/35421 · Cited by 56
Metadata
- Authors
- Prevedello, L. M., Halabi, S. S., Shih, G., Wu, C. C., Kohli, M. D., Chokshi, F. H., Erickson, B. J., Kalpathy-Cramer, J., Andriole, K. P., & Flanders, A. E.
- Venue
- Radiology: Artificial Intelligence
- Published
- 2019-01
- Volume
- 1
- Issue
- 1
- Pages
- e180031
- ISSN
- 2638-6100 (ISSN-L)
- OpenAlex
- W2911436281
- Cited by
- 169
- References
- 7
- FWCI
- 4.19
- Type
- journal article
- Language
- en
- Open access
- green
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- Prevedello, L. M., Halabi, S. S., Shih, G., Wu, C. C., Kohli, M. D., Chokshi, F. H., Erickson, B. J., Kalpathy-Cramer, J., Andriole, K. P., & Flanders, A. E. (2019). Challenges Related to Artificial Intelligence Research in Medical Imaging and the Importance of Image Analysis Competitions. Radiology: Artificial Intelligence. https://doi.org/10.1148/ryai.2019180031
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