Diego Ardila
Impact in
- Health Informatics top 0.5%
- Artificial Intelligence in Healthcare and Education
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- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
Papers in
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- Lung Cancer Diagnosis and Treatment 2
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- Face Recognition and Perception 2
- Neural dynamics and brain function 2
- Visual perception and processing mechanisms 2
- Co-authors
- Greg S. Corrado (3 shared papers)Mozziyar Etemadi (1 shared paper)Wenxing Ye (1 shared paper)Atilla P. Kiraly (2 shared papers)Joshua Reicher (2 shared papers)David P. Naidich (1 shared paper)Sujeeth Bharadwaj (2 shared papers)Lily Peng (2 shared papers)
- Journals
- Nature Medicine (1 paper)PLoS Computational Biology (1 paper)PLOS Global Public Health (1 paper)DSpace@MIT (Massachusetts Institute of Technology) (1 paper)
- Partner nations
- United States
In The Last Decade
Diego Ardila
6 papers receiving 1.7k citations
Diego Ardila's Hit Papers
Peers
Comparison fields: 5 of 144
- Health Informatics 175
- Radiology, Nuclear Medicine and Imaging 670
- Cognitive Neuroscience 303
- Pulmonary and Respiratory Medicine 412
- Artificial Intelligence 428
Countries citing papers authored by Diego Ardila
This map shows the geographic impact of Diego Ardila's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Diego Ardila with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Diego Ardila more than expected).
Fields of papers citing papers by Diego Ardila
This network shows the impact of papers produced by Diego Ardila. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Diego Ardila. The network helps show where Diego Ardila may publish in the future.
Co-authors
The 25 scholars most cited alongside Diego Ardila, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography Hit paper breakdown → | 2019 | 1274 |
| 2 | 2014 | 397 | |
| 3 | Audio Deepdream: Optimizing raw audio with convolutional networks | 2016 | 12 |
| 4 | 2024 | 8 | |
| 5 | Improving the specificity of lung cancer screening CT using deep learning | 2018 | 1 |
| 6 | 2014 | 1 |
About Diego Ardila
Diego Ardila is a scholar working on Pulmonary and Respiratory Medicine, Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging, Cardiology and Cardiovascular Medicine and Computer Vision and Pattern Recognition, having authored 6 papers that have together received 1.7k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (2 papers), Face Recognition and Perception (2 papers), Neural dynamics and brain function (2 papers), Lung Cancer Diagnosis and Treatment (2 papers), Visual perception and processing mechanisms (2 papers), Speech and Audio Processing (1 paper), Music Technology and Sound Studies (1 paper) and Non-Invasive Vital Sign Monitoring (1 paper). The work is most often cited by research in Health Informatics (175 citations), Radiology, Nuclear Medicine and Imaging (670 citations), Cognitive Neuroscience (303 citations), Pulmonary and Respiratory Medicine (412 citations) and Artificial Intelligence (428 citations). Diego Ardila has collaborated with scholars based in United States. Frequent co-authors include Greg S. Corrado, Mozziyar Etemadi, Wenxing Ye, Atilla P. Kiraly, Joshua Reicher, David P. Naidich, Sujeeth Bharadwaj, Lily Peng, Daniel Tse and Nicolas Pinto. Their work appears in journals such as Nature Medicine, PLoS Computational Biology, PLOS Global Public Health and DSpace@MIT (Massachusetts Institute of Technology).
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.