Artificial intelligence in gastrointestinal and hepatic imaging: past, present and future scopes
Clinical Imaging, ISSN: 0899-7071, Vol: 87, Page: 43-53
2022
- 3Citations
- 22Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations3
- Citation Indexes3
- CrossRef1
- Captures22
- Readers22
- 22
Review Description
The use of technology in medicine has grown exponentially because of the technological advancements allowing the digitization of medical data and optimization of their processing to extract multiple features of significant clinical relevance. Radiology has benefited substantially from technical developments and innovations, such as artificial intelligence (AI). This article describes the subsets of AI methods relevant to gastrointestinal and hepatic imaging with examples. We also discuss the evolution of AI, the current challenges, and prospects for further development in the field.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0899707122001048; http://dx.doi.org/10.1016/j.clinimag.2022.04.007; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85130136262&origin=inward; http://www.ncbi.nlm.nih.gov/pubmed/35487161; https://linkinghub.elsevier.com/retrieve/pii/S0899707122001048; https://dx.doi.org/10.1016/j.clinimag.2022.04.007
Elsevier BV
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know