Mahsa Rezaei

536 citations
6 papers · 309 indexed · 3 hit papers · h-index 4

Mahsa Rezaei

6 papers receiving 290 citations

Hit Papers

Opportunities and challenges of artificial intelligence a...1012023202620242025255075100

Peers

Mahsa Rezaei
Comparison fields: 5 of 99
  • Health Informatics 29
  • Health Information Management 29
  • Drug Discovery 1
  • Artificial Intelligence 109
  • Neurology 22
Replace Tae Joon Jun with:
Tae Joon Jun South Korea
Samir Abdelrazek Egypt
V. Mahalakshmi India
Karisma Trinanda Putra Indonesia
Osama R. Shahin Saudi Arabia
Yan Qiang China
Sarina Aminizadeh Iran
Mohamed Yaseen Jabarulla South Korea
Mansour Esmaeilpour Iran
Amr Abozeid Saudi Arabia
Mahsa Rezaei relative to Tae Joon Jun South Korea Tae Joon Jun's profile →
Citations per field
00.5×1.5×1.8×
Tae Joon Jun · 1×
Citations per year

Countries citing papers authored by Mahsa Rezaei

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

The 21 scholars most cited alongside Mahsa Rezaei, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Mahsa Rezaei Line = papers co-authored together Mahsa Rezaei links everyone, so they are left out of the graph.

All Works

6 of 6 papers shown
#Work
1
Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare servicebreakdown →
2024101
2
The applications of machine learning techniques in medical data processing based on distributed computing and the Internet of Thingsbreakdown →
2023105
3
A new lung cancer detection method based on the chest CT images using Federated Learning and blockchain systemsbreakdown →
202380
4 20203
5
Stroke subtypes, risk factors and mortality rate in northwest of Iran.
201719
6
Google Cardboard anterior and posterior segment imaging: a valuable tool for limited-resource settings
20151

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), Acute Ischemic Stroke Management (2 papers), Radiomics and Machine Learning in Medical Imaging (1 paper), Ophthalmology and Visual Health Research (1 paper), Machine Learning in Healthcare (1 paper), Radiology practices and education (1 paper) and Lung Cancer Diagnosis and Treatment (1 paper). 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.

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.

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2026