David Bermejo-Peláez
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- COVID-19 diagnosis using AI 8
- Radiomics and Machine Learning in Medical Imaging 6
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- Lung Cancer Diagnosis and Treatment 8
- Chronic Obstructive Pulmonary Disease (COPD) Research 2
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- Digital Imaging for Blood Diseases 4
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- Advanced X-ray and CT Imaging 3
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- Phytoplasmas and Hemiptera pathogens 2
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- Parasitic Diseases Research and Treatment 2
- Co-authors
- María J. Ledesma‐CarbayoRaúl San Jośe EstéparGeorge R. WashkoDaniel Jiménez‐CarreteroPietro NardelliFarbod N. RahaghiSamuel Y. AshMiguel Luengo-Oroz
- Cited by
- Health InformaticsRadiology, Nuclear Medicine and ImagingPulmonary and Respiratory Medicine
- Journals
- Blood (3 papers)Scientific Reports (2 papers)IEEE Transactions on Medical Imaging (1 paper)
- Partner nations
- SpainUnited StatesFrance
In The Last Decade
David Bermejo-Peláez
17 papers receiving 327 citations
Peers
Comparison fields: 5 of 75
- Health Informatics 11
- Radiology, Nuclear Medicine and Imaging 176
- Pulmonary and Respiratory Medicine 145
- Computer Vision and Pattern Recognition 51
- Health Information Management 9
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
The 25 scholars most cited alongside David Bermejo-Peláez, 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 | 2024 | 0 | |
| 2 | 2024 | 7 | |
| 3 | 2024 | 1 | |
| 4 | 2024 | 4 | |
| 5 | 2023 | 15 | |
| 6 | 2023 | 2 | |
| 7 | 2023 | 21 | |
| 8 | 2023 | 4 | |
| 9 | 2023 | 2 | |
| 10 | 2022 | 12 | |
| 11 | 2022 | 10 | |
| 12 | 2022 | 0 | |
| 13 | 2021 | 29 | |
| 14 | 2020 | 63 | |
| 15 | 2019 | 5 | |
| 16 | 2018 | 25 | |
| 17 | 2018 | 115 | |
| 18 | 2018 | 15 | |
| 19 | 2017 | 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), Radiomics and Machine Learning in Medical Imaging (6 papers), Digital Imaging for Blood Diseases (4 papers), Advanced X-ray and CT Imaging (3 papers), Phytoplasmas and Hemiptera pathogens (2 papers), Parasitic Diseases Research and Treatment (2 papers) and Chronic Obstructive Pulmonary Disease (COPD) Research (2 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.