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Artificial Intelligence in Medicine and Cardiac Imaging: Harnessing Big Data and Advanced Computing to Provide Personalized Medical Diagnosis and Treatment

Overview of attention for article published in Current Cardiology Reports, December 2013
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#29 of 583)
  • High Attention Score compared to outputs of the same age (90th percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

Mentioned by

blogs
1 blog
policy
1 policy source
twitter
5 tweeters

Citations

dimensions_citation
64 Dimensions

Readers on

mendeley
291 Mendeley
citeulike
1 CiteULike
Title
Artificial Intelligence in Medicine and Cardiac Imaging: Harnessing Big Data and Advanced Computing to Provide Personalized Medical Diagnosis and Treatment
Published in
Current Cardiology Reports, December 2013
DOI 10.1007/s11886-013-0441-8
Pubmed ID
Authors

Steven E. Dilsizian, Eliot L. Siegel

Abstract

Although advances in information technology in the past decade have come in quantum leaps in nearly every aspect of our lives, they seem to be coming at a slower pace in the field of medicine. However, the implementation of electronic health records (EHR) in hospitals is increasing rapidly, accelerated by the meaningful use initiatives associated with the Center for Medicare & Medicaid Services EHR Incentive Programs. The transition to electronic medical records and availability of patient data has been associated with increases in the volume and complexity of patient information, as well as an increase in medical alerts, with resulting "alert fatigue" and increased expectations for rapid and accurate diagnosis and treatment. Unfortunately, these increased demands on health care providers create greater risk for diagnostic and therapeutic errors. In the near future, artificial intelligence (AI)/machine learning will likely assist physicians with differential diagnosis of disease, treatment options suggestions, and recommendations, and, in the case of medical imaging, with cues in image interpretation. Mining and advanced analysis of "big data" in health care provide the potential not only to perform "in silico" research but also to provide "real time" diagnostic and (potentially) therapeutic recommendations based on empirical data. "On demand" access to high-performance computing and large health care databases will support and sustain our ability to achieve personalized medicine. The IBM Jeopardy! Challenge, which pitted the best all-time human players against the Watson computer, captured the imagination of millions of people across the world and demonstrated the potential to apply AI approaches to a wide variety of subject matter, including medicine. The combination of AI, big data, and massively parallel computing offers the potential to create a revolutionary way of practicing evidence-based, personalized medicine.

Twitter Demographics

The data shown below were collected from the profiles of 5 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 291 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 3 1%
Brazil 3 1%
Finland 1 <1%
France 1 <1%
United Kingdom 1 <1%
Ireland 1 <1%
Norway 1 <1%
Sweden 1 <1%
Spain 1 <1%
Other 0 0%
Unknown 278 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 53 18%
Student > Master 50 17%
Researcher 37 13%
Student > Bachelor 35 12%
Unspecified 33 11%
Other 83 29%
Readers by discipline Count As %
Medicine and Dentistry 60 21%
Unspecified 58 20%
Computer Science 54 19%
Engineering 31 11%
Business, Management and Accounting 17 6%
Other 71 24%

Attention Score in Context

This research output has an Altmetric Attention Score of 13. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 October 2018.
All research outputs
#1,201,312
of 13,576,937 outputs
Outputs from Current Cardiology Reports
#29
of 583 outputs
Outputs of similar age
#24,034
of 256,044 outputs
Outputs of similar age from Current Cardiology Reports
#1
of 23 outputs
Altmetric has tracked 13,576,937 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 583 research outputs from this source. They receive a mean Attention Score of 4.2. This one has done particularly well, scoring higher than 95% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 256,044 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 23 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.