| Title |
Deep learning-enabled medical computer vision
|
|---|---|
| Published in |
npj Digital Medicine, January 2021
|
| DOI | 10.1038/s41746-020-00376-2 |
| Pubmed ID | |
| Authors |
Andre Esteva, Katherine Chou, Serena Yeung, Nikhil Naik, Ali Madani, Ali Mottaghi, Yun Liu, Eric Topol, Jeff Dean, Richard Socher |
X Demographics
The data shown below were collected from the profiles of 434 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 103 | 24% |
| India | 19 | 4% |
| United Kingdom | 18 | 4% |
| Spain | 12 | 3% |
| Germany | 10 | 2% |
| Canada | 9 | 2% |
| Brazil | 7 | 2% |
| France | 5 | 1% |
| Australia | 5 | 1% |
| Other | 66 | 15% |
| Unknown | 180 | 41% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 317 | 73% |
| Scientists | 96 | 22% |
| Practitioners (doctors, other healthcare professionals) | 15 | 3% |
| Science communicators (journalists, bloggers, editors) | 6 | 1% |
Mendeley readers
The data shown below were compiled from readership statistics for 911 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 911 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 115 | 13% |
| Researcher | 97 | 11% |
| Student > Master | 83 | 9% |
| Student > Bachelor | 55 | 6% |
| Student > Doctoral Student | 33 | 4% |
| Other | 139 | 15% |
| Unknown | 389 | 43% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 172 | 19% |
| Engineering | 115 | 13% |
| Medicine and Dentistry | 62 | 7% |
| Unspecified | 22 | 2% |
| Agricultural and Biological Sciences | 19 | 2% |
| Other | 110 | 12% |
| Unknown | 411 | 45% |
Attention Score in Context
This research output has an Altmetric Attention Score of 312. 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 09 May 2024.
All research outputs
#112,554
of 25,905,864 outputs
Outputs from npj Digital Medicine
#32
of 1,055 outputs
Outputs of similar age
#3,293
of 531,020 outputs
Outputs of similar age from npj Digital Medicine
#2
of 37 outputs
Altmetric has tracked 25,905,864 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,055 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 55.5. This one has done particularly well, scoring higher than 96% 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 531,020 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 99% of its contemporaries.
We're also able to compare this research output to 37 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 94% of its contemporaries.