Mahsa Rezaei
- Artificial Intelligence top 10%
- Radiology, Nuclear Medicine and Imaging
- Computer Networks and Communications
- Information Systems top 10%
- Health Information Management top 5%
- Co-authors
- Arash HeidariShiva ToumajMehmet ÜnalSarina AminizadehNima Jafari NavimipourDanial JavaheriFabio StroppaMahshid Dehghan
- Topics
- COVID-19 diagnosis using AI (3 papers)Artificial Intelligence in Healthcare (2 papers)Acute Ischemic Stroke Management (2 papers)
In The Last Decade
Mahsa Rezaei
6 papers receiving 290 citations
Hit Papers
Peers
Comparison fields: 5 of 99
- Artificial Intelligence 109
- Radiology, Nuclear Medicine and Imaging 60
- Computer Networks and Communications 53
- Information Systems 48
- Health Information Management 29
Countries citing papers authored by Mahsa Rezaei
This map shows the geographic impact of Mahsa Rezaei'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 Mahsa Rezaei with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mahsa Rezaei more than expected).
Fields of papers citing papers by Mahsa Rezaei
This network shows the impact of papers produced by Mahsa Rezaei. 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 Mahsa Rezaei. The network helps show where Mahsa Rezaei may publish in the future.
Co-authorship network of co-authors of Mahsa Rezaei
This figure shows the co-authorship network connecting the top 25 collaborators of Mahsa Rezaei. A scholar is included among the top collaborators of Mahsa Rezaei 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 Mahsa Rezaei. Mahsa Rezaei is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare servicebreakdown → | 101 |
| 2 | The applications of machine learning techniques in medical data processing based on distributed computing and the Internet of Thingsbreakdown → | 105 |
| 3 | A new lung cancer detection method based on the chest CT images using Federated Learning and blockchain systemsbreakdown → | 80 |
| 4 | 3 | |
| 5 | Stroke subtypes, risk factors and mortality rate in northwest of Iran. | 19 |
| 6 | Google Cardboard anterior and posterior segment imaging: a valuable tool for limited-resource settings | 1 |
About Mahsa Rezaei
Mahsa Rezaei is a scholar working on Health Information Management, Radiology, Nuclear Medicine and Imaging and Neurology, having authored 6 papers that have together received 309 indexed citations. Recurring topics across this work include COVID-19 diagnosis using AI (3 papers), Artificial Intelligence in Healthcare (2 papers) and Acute Ischemic Stroke Management (2 papers). The work is most often cited by research in Health Informatics (29 citations), Health Information Management (29 citations) and Drug Discovery (1 citation). Mahsa Rezaei has collaborated with scholars based in Iran, Türkiye and Taiwan. Frequent co-authors include Arash Heidari, Shiva Toumaj, Mehmet Ünal, Sarina Aminizadeh, Nima Jafari Navimipour, Danial Javaheri, Fabio Stroppa, Mahshid Dehghan, Nima Jafari Navimipour and Mehdi Darbandi. Their work appears in journals such as Investigative Ophthalmology & Visual Science, Computer Methods and Programs in Biomedicine and Artificial Intelligence in Medicine.
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.