Preparing Physicians of the Future: Incorporating Data Science into Medical Education
Medical Science Educator, ISSN: 2156-8650, Vol: 34, Issue: 6, Page: 1565-1570
2024
- 2Captures
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.
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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
- Captures2
- Readers2
Article Description
The recent excitement surrounding artificial intelligence (AI) in health care underscores the importance of physician engagement with new technologies. Future clinicians must develop a strong understanding of data science (DS) to further enhance patient care. However, DS remains largely absent from medical school curricula, even though it is recognized as vital by medical students and residents alike. Here, we evaluate the current DS landscape in medical education and illustrate its impact in medicine through examples in pathology classification and sepsis detection. We also explore reasons for the exclusion of DS and propose solutions to integrate it into existing medical education frameworks.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85201235133&origin=inward; http://dx.doi.org/10.1007/s40670-024-02137-2; http://www.ncbi.nlm.nih.gov/pubmed/39758456; https://link.springer.com/10.1007/s40670-024-02137-2; https://dx.doi.org/10.1007/s40670-024-02137-2; https://link.springer.com/article/10.1007/s40670-024-02137-2
Springer Science and Business Media LLC
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