Luis Bote-Curiel
Impact in
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- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
Papers in
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- Machine Learning in Healthcare 2
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- COVID-19 diagnosis using AI 2
- Co-authors
- Francisco Miguel Sanchez-Margallo (2 shared papers)José Blas Pagador (2 shared papers)José Luis Rojo‐Álvarez (10 shared papers)Luisa F. Sánchez‐Peralta (2 shared papers)Artzai Picón (1 shared paper)Sergio Muñoz-Romero (5 shared papers)Fernando Obelleiro (2 shared papers)Jose Manuel Taboada (2 shared papers)
- Journals
- IEEE Access (4 papers)Engineering Applications of Artificial Intelligence (2 papers)Artificial Intelligence in Medicine (1 paper)Expert Systems with Applications (1 paper)Applied Sciences (1 paper)
- Partner nations
- Spain
In The Last Decade
Luis Bote-Curiel
12 papers receiving 296 citations
Peers
Comparison fields: 5 of 86
- Health Informatics 9
- Radiology, Nuclear Medicine and Imaging 93
- Health Information Management 15
- Oncology 82
- Computer Vision and Pattern Recognition 60
Countries citing papers authored by Luis Bote-Curiel
This map shows the geographic impact of Luis Bote-Curiel'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 Luis Bote-Curiel with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Luis Bote-Curiel more than expected).
Fields of papers citing papers by Luis Bote-Curiel
This network shows the impact of papers produced by Luis Bote-Curiel. 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 Luis Bote-Curiel. The network helps show where Luis Bote-Curiel may publish in the future.
Co-authors
The 19 scholars most cited alongside Luis Bote-Curiel, 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 | 2020 | 139 | |
| 2 | 2019 | 86 | |
| 3 | 2013 | 21 | |
| 4 | 2012 | 14 | |
| 5 | 2022 | 11 | |
| 6 | 2021 | 8 | |
| 7 | 2024 | 8 | |
| 8 | 2021 | 5 | |
| 9 | 2021 | 5 | |
| 10 | 2025 | 3 | |
| 11 | 2025 | 3 | |
| 12 | 2024 | 2 | |
| 13 | 2023 | 0 | |
| 14 | 2026 | 0 |
About Luis Bote-Curiel
Luis Bote-Curiel is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Health Information Management, Computer Science Applications and Oncology, having authored 14 papers that have together received 305 indexed citations. Recurring topics across this work include Gene expression and cancer classification (3 papers), COVID-19 diagnosis using AI (2 papers), Colorectal Cancer Screening and Detection (2 papers), Machine Learning in Healthcare (2 papers), RNA modifications and cancer (1 paper), Advanced Antenna and Metasurface Technologies (1 paper), Advanced Fiber Optic Sensors (1 paper) and Machine Learning in Bioinformatics (1 paper). The work is most often cited by research in Health Informatics (9 citations), Radiology, Nuclear Medicine and Imaging (93 citations), Health Information Management (15 citations), Oncology (82 citations) and Computer Vision and Pattern Recognition (60 citations). Luis Bote-Curiel has collaborated with scholars based in Spain. Frequent co-authors include Francisco Miguel Sanchez-Margallo, José Blas Pagador, José Luis Rojo‐Álvarez, Luisa F. Sánchez‐Peralta, Artzai Picón, Sergio Muñoz-Romero, Fernando Obelleiro, Jose Manuel Taboada, Diego Martinez Solis and Sergio Ruiz‐Llorente. Their work appears in journals such as IEEE Access, Engineering Applications of Artificial Intelligence, Artificial Intelligence in Medicine, Expert Systems with Applications and Applied Sciences.
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.