Toward an “Equitable” Assimilation of Artificial Intelligence and Machine Learning Into Our Health Care System
North Carolina Medical Journal, ISSN: 0029-2559, Vol: 85, Issue: 4, Page: 246-250
2024
- 1Citations
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
- Citations1
- Citation Indexes1
Article Description
Enthusiasm about the promise of artificial intelligence and machine learning in health care must be accompanied by oversight and remediation of any potential adverse effects on health equity goals that these technologies may create. We describe five equity imperatives for the use of AI/ML in health care that require attention from health care profes-sionals, developers, and policymakers.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85200024938&origin=inward; http://dx.doi.org/10.18043/001c.120565; http://www.ncbi.nlm.nih.gov/pubmed/39466092; https://ncmedicaljournal.com/article/120565-toward-an-equitable-assimilation-of-artificial-intelligence-and-machine-learning-into-our-health-care-system; https://dx.doi.org/10.18043/001c.120565
North Carolina Institute of Medicine
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