Clifford Yang
Impact in
- Health Informatics top 5%
-
- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
- Cardiac Imaging and Diagnostics
Papers in
-
- Radiomics and Machine Learning in Medical Imaging 11
- COVID-19 diagnosis using AI 6
- Cardiac Imaging and Diagnostics 2
-
- AI in cancer detection 14
- Co-authors
- Sheida NabaviReda A. AmmarTianyu WangJun BaiYufeng ZhengJinbo BiErick AvelarSusan Tannenbaum
- Journals
- Medical Physics (2 papers)BMC Bioinformatics (2 papers)Radiology (1 paper)Critical Care (1 paper)Neurocase (1 paper)
- Partner nations
- United StatesAustriaGreece
In The Last Decade
Clifford Yang
22 papers receiving 643 citations
Peers
Comparison fields: 5 of 90
- Health Informatics 29
- Radiology, Nuclear Medicine and Imaging 377
- Artificial Intelligence 356
- Neurology 79
- Computer Vision and Pattern Recognition 87
Countries citing papers authored by Clifford Yang
This map shows the geographic impact of Clifford Yang'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 Clifford Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Clifford Yang more than expected).
Fields of papers citing papers by Clifford Yang
This network shows the impact of papers produced by Clifford Yang. 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 Clifford Yang. The network helps show where Clifford Yang may publish in the future.
Co-authors
The 25 scholars most cited alongside Clifford Yang, 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 | 6 | |
| 2 | 2024 | 1 | |
| 3 | 2024 | 3 | |
| 4 | 2022 | 22 | |
| 5 | 2022 | 5 | |
| 6 | 2021 | 104 | |
| 7 | 2020 | 43 | |
| 8 | 2020 | 4 | |
| 9 | 2019 | 179 | |
| 10 | 2019 | 33 | |
| 11 | 2018 | 5 | |
| 12 | 2018 | 51 | |
| 13 | 2017 | 48 | |
| 14 | 2017 | 7 | |
| 15 | 2017 | 3 | |
| 16 | Doxorubicin-induced cardiomyopathy 17 years after chemotherapy. | 2012 | 62 |
| 17 | 2010 | 51 | |
| 18 | 2009 | 16 | |
| 19 | 2008 | 15 | |
| 20 | Germinoma-unusual presentation: a case report. | 2005 | 2 |
About Clifford Yang
Clifford Yang is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Critical Care and Intensive Care Medicine, Pulmonary and Respiratory Medicine and Anesthesiology and Pain Medicine, having authored 22 papers that have together received 673 indexed citations. Recurring topics across this work include AI in cancer detection (14 papers), Radiomics and Machine Learning in Medical Imaging (11 papers), COVID-19 diagnosis using AI (6 papers), Digital Radiography and Breast Imaging (3 papers), Cardiac Imaging and Diagnostics (2 papers), Advanced Data Compression Techniques (2 papers), Chemotherapy-induced cardiotoxicity and mitigation (2 papers) and Airway Management and Intubation Techniques (1 paper). The work is most often cited by research in Health Informatics (29 citations), Radiology, Nuclear Medicine and Imaging (377 citations), Artificial Intelligence (356 citations), Neurology (79 citations) and Computer Vision and Pattern Recognition (87 citations). Clifford Yang has collaborated with scholars based in United States, Austria and Greece. Frequent co-authors include Sheida Nabavi, Reda A. Ammar, Tianyu Wang, Jun Bai, Yufeng Zheng, Jinbo Bi, Erick Avelar, Susan Tannenbaum, Clifford G. Rios and Robert A. Arciero. Their work appears in journals such as Medical Physics, BMC Bioinformatics, Radiology, Critical Care and Neurocase.
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.