Shiva Toumaj
- Health Informatics top 2%
- Artificial Intelligence in Healthcare and Education 2
- Drug Discovery top 10%
-
- Artificial Intelligence in Healthcare 2
-
- COVID-19 diagnosis using AI 7
- Radiomics and Machine Learning in Medical Imaging 1
- Artificial Intelligence top 10%
- Machine Learning in Healthcare 4
- AI in cancer detection 2
- Explainable Artificial Intelligence (XAI) 1
-
- Brain Tumor Detection and Classification 3
Shiva Toumaj
10 papers receiving 486 citations
Hit Papers
Peers
Comparison fields: 5 of 115
- Health Informatics 57
- Drug Discovery 2
- Health Information Management 52
- Radiology, Nuclear Medicine and Imaging 149
- Artificial Intelligence 193
Countries citing papers authored by Shiva Toumaj
This map shows the geographic impact of Shiva Toumaj'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 Shiva Toumaj with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shiva Toumaj more than expected).
Fields of papers citing papers by Shiva Toumaj
This network shows the impact of papers produced by Shiva Toumaj. 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 Shiva Toumaj. The network helps show where Shiva Toumaj may publish in the future.
Co-authorship network
The 16 scholars most cited alongside Shiva Toumaj, 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 | 0 | |
| 2 | 2025 | 4 | |
| 3 | 2025 | 2 | |
| 4 | Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare servicebreakdown → | 2024 | 101 |
| 5 | 2024 | 26 | |
| 6 | The applications of machine learning techniques in medical data processing based on distributed computing and the Internet of Thingsbreakdown → | 2023 | 105 |
| 7 | A new lung cancer detection method based on the chest CT images using Federated Learning and blockchain systemsbreakdown → | 2023 | 80 |
| 8 | 2023 | 1 | |
| 9 | 2022 | 81 | |
| 10 | 2022 | 52 | |
| 11 | 2021 | 59 |
About Shiva Toumaj
Shiva Toumaj is a scholar working on Health Informatics, Health Information Management and Neurology, having authored 11 papers that have together received 511 indexed citations. Recurring topics across this work include COVID-19 diagnosis using AI (7 papers), Machine Learning in Healthcare (4 papers), Brain Tumor Detection and Classification (3 papers), Artificial Intelligence in Healthcare (2 papers), Artificial Intelligence in Healthcare and Education (2 papers), AI in cancer detection (2 papers), Radiomics and Machine Learning in Medical Imaging (1 paper) and Explainable Artificial Intelligence (XAI) (1 paper). The work is most often cited by research in Health Informatics (57 citations), Drug Discovery (2 citations) and Health Information Management (52 citations). Shiva Toumaj has collaborated with scholars based in Iran, Türkiye and Taiwan. Frequent co-authors include Arash Heidari, Mehmet Ünal, Nima Jafari Navimipour, Mahsa Rezaei, Sarina Aminizadeh, Nima Jafari Navimipour, Danial Javaheri, Fabio Stroppa, Mahshid Dehghan and Mehdi Darbandi. Their work appears in journals such as Computer Methods and Programs in Biomedicine, Neural Computing and Applications and Artificial Intelligence Review.
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.