Gabrielle Baxter

675 citations
12 papers · 364 · h-index 8

Impact in

    • Artificial Intelligence in Healthcare and Education
    • Radiomics and Machine Learning in Medical Imaging
    • MRI in cancer diagnosis
    • Advanced Neuroimaging Techniques and Applications
    • Advanced MRI Techniques and Applications
    • Medical Imaging Techniques and Applications

Papers in

Gabrielle Baxter

12 papers receiving 360 citations

Peers

Gabrielle Baxter
Comparison fields: 5 of 70
  • Health Informatics 72
  • Radiology, Nuclear Medicine and Imaging 274
  • Artificial Intelligence 110
  • Cancer Research 37
  • Pulmonary and Respiratory Medicine 72
Replace Jenika Karcich with:
Jenika Karcich United States
Lorenza Meneghetti Italy
Luoting Zhuang United States
Carolina Rossi Saccarelli United States
Sophie Chheang United States
Mathijn de Jong Netherlands
Jieun Koh South Korea
Gaia Ninatti Italy
Paula Toro United States
Caixia Sun China
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Citations per field
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Citations per year

Countries citing papers authored by Gabrielle Baxter

Since Specialization
Citations

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

Fields of papers citing papers by Gabrielle Baxter

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

12 of 12 papers shown
#Work
1 202182
2 202168
3 201966
4 202046
5 202030
6 202228
7 202019
8 202011
9 20225
10 20215
11 20233
12 20131

About Gabrielle Baxter

Gabrielle Baxter is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine, Artificial Intelligence, Cancer Research and Oncology, having authored 12 papers that have together received 364 indexed citations. Recurring topics across this work include MRI in cancer diagnosis (7 papers), Radiomics and Machine Learning in Medical Imaging (5 papers), Medical Imaging Techniques and Applications (4 papers), AI in cancer detection (3 papers), Advanced MRI Techniques and Applications (3 papers), Advanced Neuroimaging Techniques and Applications (2 papers), Breast Cancer Treatment Studies (2 papers) and Digital Radiography and Breast Imaging (2 papers). The work is most often cited by research in Health Informatics (72 citations), Radiology, Nuclear Medicine and Imaging (274 citations), Artificial Intelligence (110 citations), Cancer Research (37 citations) and Pulmonary and Respiratory Medicine (72 citations). Gabrielle Baxter has collaborated with scholars based in United Kingdom, Portugal and Austria. Frequent co-authors include Fiona J. Gilbert, Sarah Hickman, Martin J. Graves, Andrew J. Patterson, James Mackay, Ramona Woitek, Elizabeth Le, Angelica I. Avilés-Rivero, Yu Ri Im and Simon Bond. Their work appears in journals such as European Radiology, British Journal of Cancer, Radiology, Clinical Radiology and British Journal of 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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