Big data and data processing in rheumatology: bioethical perspectives
Clinical Rheumatology, ISSN: 1434-9949, Vol: 39, Issue: 4, Page: 1007-1014
2020
- 24Citations
- 93Captures
Metric Options: Counts1 Year3 YearSelecting 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
- Citations24
- Citation Indexes24
- 24
- CrossRef2
- Captures93
- Readers93
- 93
Review Description
Big data analytics and processing through artificial intelligence (AI) are increasingly being used in the health sector. This includes both clinical and research settings, and newly in specialties like rheumatology. It is, however, important to consider how these new methodologies are used, and particularly the sensitivities associated with personal information. Based on current applications in rheumatology, this article provides a narrative review of the bioethical perspectives of big data. It presents examples of databases, data analytic methods, and AI in this specialty to address four main ethical issues: privacy and confidentiality, informed consent, the impact on the medical profession, and justice. The use of big data and AI processing in healthcare has great potential to improve the quality of clinical care, including through better diagnosis, treatment, and prognosis. They may also increase patient and societal participation and engagement in healthcare and research. Developing these methodologies and using the information generated from them in line with ethical standards could positively affect the design of global health policies and introduce a new phase in the democratization of health.Key Points• Current applications of big data, data analytics, and AI in rheumatology—including registries, machine learning algorithms, and consumer-facing platforms—raise issues in four main bioethical areas: privacy and confidentiality, informed consent, the impact on the medical profession, and justice.• Bioethical concerns about rheumatology registries require careful consideration of privacy provisions, set within the context of local, national, and regional law.• Machine learning and big data aid diagnosis, treatment, and prognosis, but the final decision about the use of information from algorithms should be left to rheumatology specialists to maintain the promise of fiduciary obligations in the physician–patient relationship.• International collaboration in big data projects and increased patient engagement could be ways to counteract health inequalities in the practice of rheumatology, even on a global scale.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85079716020&origin=inward; http://dx.doi.org/10.1007/s10067-020-04969-w; http://www.ncbi.nlm.nih.gov/pubmed/32062767; http://link.springer.com/10.1007/s10067-020-04969-w; https://dx.doi.org/10.1007/s10067-020-04969-w; https://link.springer.com/article/10.1007/s10067-020-04969-w
Springer Science and Business Media LLC
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know