Junyi Gao
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- Artificial Intelligence in Healthcare 8
- Health Informatics top 10%
- Artificial Intelligence top 5%
- Machine Learning in Healthcare 13
- Topic Modeling 7
- Modeling and Simulation top 10%
- COVID-19 epidemiological studies 2
- Signal Processing top 10%
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- COVID-19 diagnosis using AI 3
- Radiomics and Machine Learning in Medical Imaging 2
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- Influenza Virus Research Studies 2
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- Surgical Simulation and Training 2
- Journals
- Journal of the American Medical Informatics Association (3 papers)Patterns (2 papers)iScience (1 paper)
- Partner nations
- ChinaUnited KingdomUnited States
In The Last Decade
Junyi Gao
26 papers receiving 452 citations
Peers
Comparison fields: 5 of 97
- Health Information Management 148
- Health Informatics 22
- Artificial Intelligence 314
- Modeling and Simulation 38
- Signal Processing 45
Countries citing papers authored by Junyi Gao
This map shows the geographic impact of Junyi Gao'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 Junyi Gao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Junyi Gao more than expected).
Fields of papers citing papers by Junyi Gao
This network shows the impact of papers produced by Junyi Gao. 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 Junyi Gao. The network helps show where Junyi Gao may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Junyi Gao, 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 | 2025 | 0 | |
| 2 | 2025 | 1 | |
| 3 | 2025 | 3 | |
| 4 | 2025 | 1 | |
| 5 | 2025 | 1 | |
| 6 | 2025 | 0 | |
| 7 | 2025 | 0 | |
| 8 | 2025 | 0 | |
| 9 | 2024 | 9 | |
| 10 | 2024 | 4 | |
| 11 | 2024 | 2 | |
| 12 | 2024 | 1 | |
| 13 | 2023 | 11 | |
| 14 | 2023 | 8 | |
| 15 | 2023 | 14 | |
| 16 | 2022 | 15 | |
| 17 | 2022 | 11 | |
| 18 | 2020 | 98 | |
| 19 | 2018 | 4 | |
| 20 | Application of Stereographic Projection in Analysis of Stability of Certain Tunner Portal Slope | 2008 | 1 |
About Junyi Gao
Junyi Gao is a scholar working on Health Information Management, General Engineering, Health Informatics, Artificial Intelligence and Modeling and Simulation, having authored 30 papers that have together received 469 indexed citations. Recurring topics across this work include Machine Learning in Healthcare (13 papers), Artificial Intelligence in Healthcare (8 papers), Topic Modeling (7 papers), COVID-19 diagnosis using AI (3 papers), Radiomics and Machine Learning in Medical Imaging (2 papers), Influenza Virus Research Studies (2 papers), COVID-19 epidemiological studies (2 papers) and Surgical Simulation and Training (2 papers). The work is most often cited by research in Health Information Management (148 citations), Health Informatics (22 citations), Artificial Intelligence (314 citations), Modeling and Simulation (38 citations) and Signal Processing (45 citations). Junyi Gao has collaborated with scholars based in China, United Kingdom and United States. Frequent co-authors include Yasha Wang, Liantao Ma, Jimeng Sun, Jiangtao Wang, Lucas M. Glass, Cao Xiao, Xinyu Ma, Wenjie Ruan, Wen Tang and Xin Gao. Their work appears in journals such as Journal of the American Medical Informatics Association, Patterns, iScience, Nature Communications and Engineering Structures.
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.