Chaoyi Wu
- Health Informatics top 2%
- Artificial Intelligence top 10%
- Topic Modeling 6
- Machine Learning in Healthcare 4
- AI in cancer detection 4
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- COVID-19 diagnosis using AI 5
- Radiomics and Machine Learning in Medical Imaging 3
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- Multimodal Machine Learning Applications 4
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- Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes 2
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- Metabolomics and Mass Spectrometry Studies 2
- Co-authors
- Ya ZhangXiaoman ZhangWeidi XieYanfeng WangWeixiong LinPengcheng QiuHaicheng WangEkram Hossain
- Journals
- Nature Communications (5 papers)SHILAP Revista de lepidopterología (1 paper)Science Advances (1 paper)
- Partner nations
- ChinaUnited StatesGermany
In The Last Decade
Chaoyi Wu
18 papers receiving 420 citations
Hit Papers
Peers
Comparison fields: 5 of 89
- Health Informatics 78
- Artificial Intelligence 200
- Radiology, Nuclear Medicine and Imaging 108
- Computer Vision and Pattern Recognition 65
- Health Information Management 11
Countries citing papers authored by Chaoyi Wu
This map shows the geographic impact of Chaoyi Wu'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 Chaoyi Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chaoyi Wu more than expected).
Fields of papers citing papers by Chaoyi Wu
This network shows the impact of papers produced by Chaoyi Wu. 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 Chaoyi Wu. The network helps show where Chaoyi Wu may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Chaoyi Wu, 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 | 0 | |
| 4 | 2025 | 1 | |
| 5 | 2025 | 11 | |
| 6 | 2025 | 0 | |
| 7 | 2025 | 3 | |
| 8 | 2025 | 8 | |
| 9 | 2024 | 5 | |
| 10 | 2024 | 5 | |
| 11 | 2024 | 38 | |
| 12 | 2024 | 4 | |
| 13 | 2024 | 9 | |
| 14 | PMC-LLaMA: toward building open-source language models for medicinebreakdown → | 2024 | 132 |
| 15 | 2023 | 86 | |
| 16 | 2023 | 1 | |
| 17 | 2022 | 3 | |
| 18 | 2020 | 46 | |
| 19 | 2020 | 12 | |
| 20 | 2019 | 18 |
About Chaoyi Wu
Chaoyi Wu is a scholar working on Health Informatics, Radiology, Nuclear Medicine and Imaging and Computer Vision and Pattern Recognition, having authored 21 papers that have together received 426 indexed citations. Recurring topics across this work include Topic Modeling (6 papers), COVID-19 diagnosis using AI (5 papers), Machine Learning in Healthcare (4 papers), AI in cancer detection (4 papers), Multimodal Machine Learning Applications (4 papers), Radiomics and Machine Learning in Medical Imaging (3 papers), Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes (2 papers) and Metabolomics and Mass Spectrometry Studies (2 papers). The work is most often cited by research in Health Informatics (78 citations), Artificial Intelligence (200 citations) and Radiology, Nuclear Medicine and Imaging (108 citations). Chaoyi Wu has collaborated with scholars based in China, United States and Germany. Frequent co-authors include Ya Zhang, Xiaoman Zhang, Weidi Xie, Yanfeng Wang, Weidi Xie, Weixiong Lin, Yanfeng Wang, Xiaoman Zhang, Pengcheng Qiu and Haicheng Wang. Their work appears in journals such as Nature Communications, SHILAP Revista de lepidopterología and Science Advances.
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.