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
- David P. Naidich (1 shared paper)Mozziyar Etemadi (1 shared paper)Sujeeth Bharadwaj (2 shared papers)Wenxing Ye (1 shared paper)Greg S. Corrado (3 shared papers)Safal Shetty (1 shared paper)Joshua Reicher (2 shared papers)Atilla P. Kiraly (2 shared papers)
- Journals
- Nature Medicine (1 paper)PLoS Computational Biology (1 paper)SHILAP Revista de lepidopterología (1 paper)DSpace@MIT (Massachusetts Institute of Technology) (1 paper)
- Partner nations
- United States
In The Last Decade
Diego Ardila
6 papers receiving 1.6k citations
Diego Ardila's Hit Papers
Peers
Comparison fields: 5 of 147
- Health Informatics 197
- Radiology, Nuclear Medicine and Imaging 839
- Cognitive Neuroscience 302
- Artificial Intelligence 489
- Pulmonary and Respiratory Medicine 481
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 | 1221 |
| 2 | Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition Hit paper breakdown → | 2014 | 391 |
| 3 | Audio Deepdream: Optimizing raw audio with convolutional networks | 2016 | 12 |
| 4 | 2024 | 6 | |
| 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 1.6k indexed citations. Recurring topics across this work include Lung Cancer Diagnosis and Treatment (2 papers), Face Recognition and Perception (2 papers), Radiomics and Machine Learning in Medical Imaging (2 papers), Neural dynamics and brain function (2 papers), Visual perception and processing mechanisms (2 papers), ECG Monitoring and Analysis (1 paper), Non-Invasive Vital Sign Monitoring (1 paper) and COVID-19 diagnosis using AI (1 paper). The work is most often cited by research in Health Informatics (197 citations), Radiology, Nuclear Medicine and Imaging (839 citations), Cognitive Neuroscience (302 citations), Artificial Intelligence (489 citations) and Pulmonary and Respiratory Medicine (481 citations). Diego Ardila has collaborated with scholars based in United States. Frequent co-authors include David P. Naidich, Mozziyar Etemadi, Sujeeth Bharadwaj, Wenxing Ye, Greg S. Corrado, Safal Shetty, Joshua Reicher, Atilla P. Kiraly, Lily Peng and Daniel Tse. Their work appears in journals such as Nature Medicine, PLoS Computational Biology, SHILAP Revista de lepidopterología 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.