Ghada Zamzmi

1.3k citations
54 papers · 649 indexed · h-index 16
Topics
Pediatric Pain Management Techniques (14 papers)COVID-19 diagnosis using AI (14 papers)Radiomics and Machine Learning in Medical Imaging (13 papers)
Journals
SHILAP Revista de lepidopterologíaPLoS ONEExpert Systems with Applications

In The Last Decade

Ghada Zamzmi

54 papers receiving 636 citations

Peers

Ghada Zamzmi
Comparison fields: 5 of 99
  • Radiology, Nuclear Medicine and Imaging 197
  • Pediatrics, Perinatology and Child Health 181
  • Artificial Intelligence 127
  • Pharmacy 96
  • Computer Vision and Pattern Recognition 92
Replace João Jorge with:
João Jorge United Kingdom
Sitthichok Chaichulee Thailand
Rohan Joshi Netherlands
Agnese Sbrollini Italy
Clifton R. Haider United States
Micaela Morettini Italy
Jilong Kuang United States
Kicky G. van Leeuwen Netherlands
Greg O’Grady New Zealand
Viswam Nathan United States
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Citations per field
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Citations per year

Countries citing papers authored by Ghada Zamzmi

Since Specialization
Citations

This map shows the geographic impact of Ghada Zamzmi'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 Ghada Zamzmi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ghada Zamzmi more than expected).

Fields of papers citing papers by Ghada Zamzmi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ghada Zamzmi. 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 Ghada Zamzmi. The network helps show where Ghada Zamzmi may publish in the future.

Co-authorship network of co-authors of Ghada Zamzmi

This figure shows the co-authorship network connecting the top 25 collaborators of Ghada Zamzmi. A scholar is included among the top collaborators of Ghada Zamzmi 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 Ghada Zamzmi. Ghada Zamzmi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 5
2 2
3 2
4 1
5 3
6 3
7 6
8 25
9 31
10 4
11 6
12 5
13 3
14 1
15 19
16 17
17 51
18 33
19 14
20 72

About Ghada Zamzmi

Ghada Zamzmi is a scholar working on Health Informatics, Pharmacy and Radiology, Nuclear Medicine and Imaging, having authored 54 papers that have together received 649 indexed citations. Recurring topics across this work include Pediatric Pain Management Techniques (14 papers), COVID-19 diagnosis using AI (14 papers) and Radiomics and Machine Learning in Medical Imaging (13 papers). The work is most often cited by research in Health Informatics (49 citations), Pharmacy (96 citations) and Pediatrics, Perinatology and Child Health (181 citations). Ghada Zamzmi has collaborated with scholars based in United States, China and United Kingdom. Frequent co-authors include Sameer Antani, Dmitry B. Goldgof, Yu Sun, Sivaramakrishnan Rajaraman, Rangachar Kasturi, Terri Ashmeade, Ruicong Zhi, Thao Ho, Vandana Sachdev and Les Folio. Their work appears in journals such as SHILAP Revista de lepidopterología, PLoS ONE and Expert Systems with Applications.

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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