David Bermejo-Peláez
- Radiology, Nuclear Medicine and Imaging top 10%
- Pulmonary and Respiratory Medicine
- Artificial Intelligence
- Biomedical Engineering
- Computer Vision and Pattern Recognition
- Co-authors
- María J. Ledesma‐CarbayoRaúl San Jośe EstéparGeorge R. WashkoDaniel Jiménez‐CarreteroPietro NardelliFarbod N. RahaghiSamuel Y. AshMiguel Luengo-Oroz
- Topics
- Lung Cancer Diagnosis and Treatment (8 papers)COVID-19 diagnosis using AI (8 papers)Radiomics and Machine Learning in Medical Imaging (6 papers)
- Cited by
- Health InformaticsRadiology, Nuclear Medicine and ImagingPulmonary and Respiratory Medicine
- Partner nations
- SpainUnited StatesFrance
In The Last Decade
David Bermejo-Peláez
17 papers receiving 327 citations
Peers
Comparison fields: 5 of 75
- Radiology, Nuclear Medicine and Imaging 176
- Pulmonary and Respiratory Medicine 145
- Artificial Intelligence 56
- Biomedical Engineering 52
- Computer Vision and Pattern Recognition 51
Countries citing papers authored by David Bermejo-Peláez
This map shows the geographic impact of David Bermejo-Peláez'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 David Bermejo-Peláez with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Bermejo-Peláez more than expected).
Fields of papers citing papers by David Bermejo-Peláez
This network shows the impact of papers produced by David Bermejo-Peláez. 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 David Bermejo-Peláez. The network helps show where David Bermejo-Peláez may publish in the future.
Co-authorship network of co-authors of David Bermejo-Peláez
This figure shows the co-authorship network connecting the top 25 collaborators of David Bermejo-Peláez. A scholar is included among the top collaborators of David Bermejo-Peláez based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with David Bermejo-Peláez. David Bermejo-Peláez is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 7 | |
| 3 | 1 | |
| 4 | 4 | |
| 5 | 15 | |
| 6 | 2 | |
| 7 | 21 | |
| 8 | 4 | |
| 9 | 2 | |
| 10 | 12 | |
| 11 | 10 | |
| 12 | 0 | |
| 13 | 29 | |
| 14 | 63 | |
| 15 | 5 | |
| 16 | 25 | |
| 17 | 115 | |
| 18 | 15 | |
| 19 | 8 |
About David Bermejo-Peláez
David Bermejo-Peláez is a scholar working on Health Informatics, Radiology, Nuclear Medicine and Imaging and Infectious Diseases, having authored 19 papers that have together received 338 indexed citations. Recurring topics across this work include Lung Cancer Diagnosis and Treatment (8 papers), COVID-19 diagnosis using AI (8 papers) and Radiomics and Machine Learning in Medical Imaging (6 papers). The work is most often cited by research in Health Informatics (11 citations), Radiology, Nuclear Medicine and Imaging (176 citations) and Pulmonary and Respiratory Medicine (145 citations). David Bermejo-Peláez has collaborated with scholars based in Spain, United States and France. Frequent co-authors include María J. Ledesma‐Carbayo, Raúl San Jośe Estépar, George R. Washko, Daniel Jiménez‐Carretero, Pietro Nardelli, Farbod N. Rahaghi, Samuel Y. Ash, Miguel Luengo-Oroz, Lin Lin and Narda Medina. Their work appears in journals such as Blood, Scientific Reports and IEEE Transactions on Medical Imaging.
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.