Jason Mancuso
Impact in
- Health Informatics top 2%
- Artificial Intelligence in Healthcare and Education
- Artificial Intelligence top 10%
- Privacy-Preserving Technologies in Data
- Cryptography and Data Security
- AI in cancer detection
- Adversarial Robustness in Machine Learning
Papers in
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- Privacy-Preserving Technologies in Data 2
- Cryptography and Data Security 1
- Stochastic Gradient Optimization Techniques 1
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- Artificial Intelligence in Healthcare and Education 1
Jason Mancuso
2 papers receiving 275 citations
Jason Mancuso's Hit Papers
Peers
Comparison fields: 5 of 65
- Health Informatics 53
- Artificial Intelligence 190
- Radiology, Nuclear Medicine and Imaging 62
- Computer Science Applications 10
- Health Information Management 7
Countries citing papers authored by Jason Mancuso
This map shows the geographic impact of Jason Mancuso'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 Jason Mancuso with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jason Mancuso more than expected).
Fields of papers citing papers by Jason Mancuso
This network shows the impact of papers produced by Jason Mancuso. 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 Jason Mancuso. The network helps show where Jason Mancuso may publish in the future.
Co-authors
The 16 scholars most cited alongside Jason Mancuso, 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 privacy preserving deep learning on multi-institutional medical imaging Hit paper breakdown → | 2021 | 270 |
| 2 | 2020 | 9 |
About Jason Mancuso
Jason Mancuso is a scholar working on Artificial Intelligence, Health Informatics, Radiology, Nuclear Medicine and Imaging, Infectious Diseases and Organic Chemistry, having authored 2 papers that have together received 279 indexed citations. Recurring topics across this work include Privacy-Preserving Technologies in Data (2 papers), Artificial Intelligence in Healthcare and Education (1 paper), COVID-19 diagnosis using AI (1 paper), Cryptography and Data Security (1 paper) and Stochastic Gradient Optimization Techniques (1 paper). The work is most often cited by research in Health Informatics (53 citations), Artificial Intelligence (190 citations), Radiology, Nuclear Medicine and Imaging (62 citations), Computer Science Applications (10 citations) and Health Information Management (7 citations). Jason Mancuso has collaborated with scholars based in Germany, Canada and Brazil. Frequent co-authors include Andrew Trask, Théo Ryffel, Dmitrii Usynin, Alexander Ziller, Friederike Jungmann, Andreas Saleh, Jonathan Passerat‐Palmbach, Georgios Kaissis, Daniel Rueckert and Rickmer Braren. Their work appears in journals such as Nature Machine Intelligence.
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.