Simukayi Mutasa

1.7k citations
38 papers · 1.2k · h-index 20

Impact in

Papers in

Simukayi Mutasa

38 papers receiving 1.2k citations

Peers

Simukayi Mutasa
Comparison fields: 5 of 124
  • Health Informatics 209
  • Radiology, Nuclear Medicine and Imaging 723
  • Artificial Intelligence 484
  • Oral Surgery 53
  • Health Information Management 31
Replace Alireza Mehrtash with:
Alireza Mehrtash United States
Phillip M. Cheng United States
Matteo Interlenghi Italy
June‐Goo Lee South Korea
Gabriel Chartrand Canada
Hyunna Lee South Korea
Keno K. Bressem Germany
J. Raymond Geis United States
Arnaldo Stanzione Italy
Shahein Tajmir United States
Simukayi Mutasa relative to Alireza Mehrtash United States Alireza Mehrtash's profile →
Citations per field
00.5×3.5×
Alireza Mehrtash · 1×
Citations per year

Countries citing papers authored by Simukayi Mutasa

Since Specialization
Citations

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

Fields of papers citing papers by Simukayi Mutasa

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Simukayi Mutasa, 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 Simukayi Mutasa Line = papers co-authored together Simukayi Mutasa links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 38 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2020160
2 201995
3 201894
4 202076
5 201869
6 201961
7 201856
8 201852
9 201947
10 201947
11 201844
12 202238
13 201836
14 202035
15 202030
16 201826
17 202026
18 202125
19 202024
20 201819

About Simukayi Mutasa

Simukayi Mutasa is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Pulmonary and Respiratory Medicine, Health Informatics and Cancer Research, having authored 38 papers that have together received 1.2k indexed citations. Recurring topics across this work include AI in cancer detection (18 papers), Radiomics and Machine Learning in Medical Imaging (18 papers), Artificial Intelligence in Healthcare and Education (8 papers), Breast Cancer Treatment Studies (7 papers), Digital Radiography and Breast Imaging (4 papers), Medical Imaging and Analysis (4 papers), MRI in cancer diagnosis (4 papers) and Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis (3 papers). The work is most often cited by research in Health Informatics (209 citations), Radiology, Nuclear Medicine and Imaging (723 citations), Artificial Intelligence (484 citations), Oral Surgery (53 citations) and Health Information Management (31 citations). Simukayi Mutasa has collaborated with scholars based in United States. Frequent co-authors include Richard Ha, Peter Chang, Sachin Jambawalikar, Shawn Sun, Michael Z. Liu, Jenika Karcich, Eduardo Pascual Van Sant, Rama S. Ayyala, Carrie Ruzal‐Shapiro and Ralph Wynn. Their work appears in journals such as Journal of Digital Imaging, American Journal of Roentgenology, Stroke, Clinical Breast Cancer and Academic Radiology.

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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