The DOI matches its Crossref record; 63 of the 63 references it cites were found in a registry or an index.
Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine
Open accessAbstract
One of the most promising areas of health innovation is the application of artificial intelligence (AI), primarily in medical imaging. This article provides basic definitions of terms such as "machine/deep learning" and analyses the integration of AI into radiology. Publications on AI have drastically increased from about 100-150 per year in 2007-2008 to 700-800 per year in 2016-2017. Magnetic resonance imaging and computed tomography collectively account for more than 50% of current articles. Neuroradiology appears in about one-third of the papers, followed by musculoskeletal, cardiovascular, breast, urogenital, lung/thorax, and abdomen, each representing 6-9% of articles. With an irreversible increase in the amount of data and the possibility to use AI to identify findings either detectable or not by the human eye, radiology is now moving from a subjective perceptual skill to a more objective science. Radiologists, who were on the forefront of the digital era in medicine, can guide the introduction of AI into healthcare. Yet, they will not be replaced because radiology includes communication of diagnosis, consideration of patient's values and preferences, medical judgment, quality assurance, education, policy-making, and interventional procedures. The higher efficiency provided by AI will allow radiologists to perform more value-added tasks, becoming more visible to patients and playing a vital role in multidisciplinary clinical teams.
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Cites
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Some Studies in Machine Learning Using the Game of Checkers
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Cited by
Lee, D., & Yoon, S. N. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18010271 · Cited by 1,017
Federated learning for predicting clinical outcomes in patients with COVID-19
Dayan, I., Roth, H. R., Zhong, A., Harouni, A., Gentili, A., Abidin, A. Z., Liu, A., Costa, A. B., Wood, B. J., Tsai, C.-S., et al. (2021) · Nature Medicine · doi:10.1038/s41591-021-01506-3 · Cited by 842
AI applications to medical images: From machine learning to deep learning
Castiglioni, I., Rundo, L., Codari, M., Di Leo, G., Salvatore, C., Interlenghi, M., Gallivanone, F., Cozzi, A., D'Amico, N. C., & Sardanelli, F. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.02.006 · Cited by 710
Self-citationPesapane, F., Volonté, C., Codari, M., & Sardanelli, F. (2018) · Insights into Imaging · doi:10.1007/s13244-018-0645-y · Cited by 479
Self-citationDi Leo, G., & Sardanelli, F. (2020) · European Radiology Experimental · doi:10.1186/s41747-020-0145-y · Cited by 455
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Metadata
- Authors
- Pesapane, F., Codari, M., & Sardanelli, F.
- Venue
- European Radiology Experimental
- Published
- 2018-10-24
- Volume
- 2
- Issue
- 1
- Pages
- 35-35
- ISSN
- 2509-9280 (ISSN-L)
- OpenAlex
- W2893693469
- Cited by
- 833
- References
- 63
- FWCI
- 10.76
- Type
- journal article
- Language
- en
- Open access
- gold
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