John Papaioannou
- Artificial Intelligence top 2%
- Radiology, Nuclear Medicine and Imaging top 5%
- Pulmonary and Respiratory Medicine top 10%
- Oncology
- Computer Vision and Pattern Recognition top 10%
- Co-authors
- Alexandra EdwardsRobert M. NishikawaMaryellen L. GigerYulei JiangKaren DrukkerKunio DoiRobert A. SchmidtMarc Inciardi
- Topics
- AI in cancer detection (24 papers)Radiomics and Machine Learning in Medical Imaging (21 papers)MRI in cancer diagnosis (14 papers)
- Partner nations
- United StatesChinaArgentina
In The Last Decade
John Papaioannou
37 papers receiving 667 citations
Peers
Comparison fields: 5 of 65
- Artificial Intelligence 505
- Radiology, Nuclear Medicine and Imaging 446
- Pulmonary and Respiratory Medicine 223
- Oncology 119
- Computer Vision and Pattern Recognition 117
Countries citing papers authored by John Papaioannou
This map shows the geographic impact of John Papaioannou'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 John Papaioannou with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John Papaioannou more than expected).
Fields of papers citing papers by John Papaioannou
This network shows the impact of papers produced by John Papaioannou. 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 John Papaioannou. The network helps show where John Papaioannou may publish in the future.
Co-authorship network of co-authors of John Papaioannou
This figure shows the co-authorship network connecting the top 25 collaborators of John Papaioannou. A scholar is included among the top collaborators of John Papaioannou 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 John Papaioannou. John Papaioannou is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 7 | |
| 4 | 41 | |
| 5 | 14 | |
| 6 | 5 | |
| 7 | 6 | |
| 8 | 50 | |
| 9 | 45 | |
| 10 | 3 | |
| 11 | 1 | |
| 12 | 43 | |
| 13 | 26 | |
| 14 | 13 | |
| 15 | 16 | |
| 16 | 1 | |
| 17 | 10 | |
| 18 | 44 | |
| 19 | 4 | |
| 20 | 43 |
About John Papaioannou
John Papaioannou is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Pulmonary and Respiratory Medicine, having authored 40 papers that have together received 690 indexed citations. Recurring topics across this work include AI in cancer detection (24 papers), Radiomics and Machine Learning in Medical Imaging (21 papers) and MRI in cancer diagnosis (14 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (446 citations), Health Informatics (22 citations) and Artificial Intelligence (505 citations). John Papaioannou has collaborated with scholars based in United States, China and Argentina. Frequent co-authors include Alexandra Edwards, Robert M. Nishikawa, Maryellen L. Giger, Yulei Jiang, Karen Drukker, Kunio Doi, Robert A. Schmidt, Marc Inciardi, Hui Li and Heather M. Whitney. Their work appears in journals such as Radiology, Journal of neurosurgery and The American Journal of Cardiology.
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.