Jiechao Ma
- Radiology, Nuclear Medicine and Imaging top 5%
- Pulmonary and Respiratory Medicine top 10%
- Artificial Intelligence top 10%
- Neurology top 10%
- Physiology
- Topics
- Radiomics and Machine Learning in Medical Imaging (14 papers)COVID-19 diagnosis using AI (10 papers)Lung Cancer Diagnosis and Treatment (8 papers)
- Journals
- Proceedings of the National Academy of SciencesSHILAP Revista de lepidopterologíaAnesthesiology
- Partner nations
- ChinaHong KongUnited States
In The Last Decade
Jiechao Ma
30 papers receiving 725 citations
Hit Papers
Peers
Comparison fields: 5 of 109
- Radiology, Nuclear Medicine and Imaging 374
- Pulmonary and Respiratory Medicine 222
- Artificial Intelligence 142
- Neurology 107
- Physiology 82
Countries citing papers authored by Jiechao Ma
This map shows the geographic impact of Jiechao Ma'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 Jiechao Ma with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jiechao Ma more than expected).
Fields of papers citing papers by Jiechao Ma
This network shows the impact of papers produced by Jiechao Ma. 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 Jiechao Ma. The network helps show where Jiechao Ma may publish in the future.
Co-authorship network of co-authors of Jiechao Ma
This figure shows the co-authorship network connecting the top 25 collaborators of Jiechao Ma. A scholar is included among the top collaborators of Jiechao Ma based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jiechao Ma. Jiechao Ma is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 14 | |
| 3 | 5 | |
| 4 | The long-term health outcomes, pathophysiological mechanisms and multidisciplinary management of long COVIDbreakdown → | 60 |
| 5 | 38 | |
| 6 | 27 | |
| 7 | 53 | |
| 8 | 16 | |
| 9 | 23 | |
| 10 | 28 | |
| 11 | 53 | |
| 12 | 2 | |
| 13 | 12 | |
| 14 | 7 | |
| 15 | Advances in application of artificial intelligence in medical image analysis | 1 |
| 16 | 73 | |
| 17 | 3 | |
| 18 | Non-Invasive Tonometry in the Mouse | 5 |
| 19 | 24 | |
| 20 | 26 |
About Jiechao Ma
Jiechao Ma is a scholar working on Radiology, Nuclear Medicine and Imaging, Health Informatics and Process Chemistry and Technology, having authored 30 papers that have together received 734 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (14 papers), COVID-19 diagnosis using AI (10 papers) and Lung Cancer Diagnosis and Treatment (8 papers). The work is most often cited by research in Health Informatics (41 citations), Radiology, Nuclear Medicine and Imaging (374 citations) and Neurology (107 citations). Jiechao Ma has collaborated with scholars based in China, Hong Kong and United States. Frequent co-authors include Chengdi Wang, Yizhou Yu, Weimin Li, Jun Shao, Rongguo Zhang, Shu Zhang, Paul L. Huang, Xi Tian, Jianlin Wu and Yang Song. Their work appears in journals such as Proceedings of the National Academy of Sciences, SHILAP Revista de lepidopterología and Anesthesiology.
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.