Derek Driggs
Impact in
- Health Informatics top 1%
- Artificial Intelligence in Healthcare and Education
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- COVID-19 diagnosis using AI
- Radiomics and Machine Learning in Medical Imaging
Papers in
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- COVID-19 diagnosis using AI 3
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- Optimization and Variational Analysis 2
- Co-authors
- Carola‐Bibiane Schönlieb (6 shared papers)Mike E. Davies (1 shared paper)Evis Sala (2 shared papers)Michael T. Roberts (2 shared papers)Jingwei Liang (1 shared paper)Junqi Tang (2 shared papers)IAN A. SELBY (1 shared paper)Judith L Babar (1 shared paper)
- Journals
- SIAM Journal on Imaging Sciences (3 papers)Radiology Artificial Intelligence (1 paper)Mathematical Programming (1 paper)SIAM Journal on Scientific Computing (1 paper)Proceedings of the IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes (1 paper)
- Partner nations
- United KingdomAustriaChina
In The Last Decade
Derek Driggs
6 papers receiving 719 citations
Derek Driggs's Hit Papers
Peers
Comparison fields: 5 of 97
- Health Informatics 185
- Radiology, Nuclear Medicine and Imaging 302
- Computational Mathematics 3
- Artificial Intelligence 160
- Health Information Management 37
Countries citing papers authored by Derek Driggs
This map shows the geographic impact of Derek Driggs'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 Derek Driggs with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Derek Driggs more than expected).
Fields of papers citing papers by Derek Driggs
This network shows the impact of papers produced by Derek Driggs. 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 Derek Driggs. The network helps show where Derek Driggs may publish in the future.
Co-authors
The 20 scholars most cited alongside Derek Driggs, 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 | Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans Hit paper breakdown → | 2020 | 681 |
| 2 | 2021 | 26 | |
| 3 | 2021 | 21 | |
| 4 | Machine learning for COVID-19 detection and prognostication using chest radiographs and CT scans: a systematic methodological review | 2020 | 14 |
| 5 | 2019 | 2 | |
| 6 | 2019 | 1 | |
| 7 | 2020 | 0 | |
| 8 | 2024 | 0 | |
| 9 | 2025 | 0 |
About Derek Driggs
Derek Driggs is a scholar working on Radiology, Nuclear Medicine and Imaging, Computational Theory and Mathematics, Numerical Analysis, Computational Mechanics and Artificial Intelligence, having authored 9 papers that have together received 745 indexed citations. Recurring topics across this work include COVID-19 diagnosis using AI (3 papers), Sparse and Compressive Sensing Techniques (2 papers), Optimization and Variational Analysis (2 papers), Stochastic Gradient Optimization Techniques (2 papers), Image and Signal Denoising Methods (1 paper) and Advanced Optimization Algorithms Research (1 paper). The work is most often cited by research in Health Informatics (185 citations), Radiology, Nuclear Medicine and Imaging (302 citations), Computational Mathematics (3 citations), Artificial Intelligence (160 citations) and Health Information Management (37 citations). Derek Driggs has collaborated with scholars based in United Kingdom, Austria and China. Frequent co-authors include Carola‐Bibiane Schönlieb, Mike E. Davies, Evis Sala, Michael T. Roberts, Jingwei Liang, Junqi Tang, IAN A. SELBY, Judith L Babar, Effrossyni Gkrania‐Klotsas and Guang Zhong Yang. Their work appears in journals such as SIAM Journal on Imaging Sciences, Radiology Artificial Intelligence, Mathematical Programming, SIAM Journal on Scientific Computing and Proceedings of the IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes.
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.