Martin Fergie

431 citations
23 papers · 257 indexed · h-index 9
Topics
AI in cancer detection (8 papers)Global Cancer Incidence and Screening (5 papers)Ocular Surface and Contact Lens (4 papers)
Journals
SHILAP Revista de lepidopterologíaBloodBritish Journal of Cancer

In The Last Decade

Martin Fergie

21 papers receiving 251 citations

Peers

Martin Fergie
Comparison fields: 5 of 68
  • Artificial Intelligence 70
  • Oncology 65
  • Radiology, Nuclear Medicine and Imaging 56
  • Immunology 40
  • Pulmonary and Respiratory Medicine 38
Replace Mani Nassir with:
Mani Nassir Germany
Sean Hacking United States
Niki Margari Greece
Shinichi Tsuchiya Japan
Mircea-Sebastian Şerbănescu Romania
Cláudia Freitas Portugal
Frederik Wessels Germany
Yinhui Deng China
Julia Arfi‐Rouche France
Jieun Koh South Korea
Martin Fergie relative to Mani Nassir Germany Mani Nassir's profile →
Citations per field
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Mani Nassir · 1×
Citations per year

Countries citing papers authored by Martin Fergie

Since Specialization
Citations

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

Fields of papers citing papers by Martin Fergie

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Martin Fergie

This figure shows the co-authorship network connecting the top 25 collaborators of Martin Fergie. A scholar is included among the top collaborators of Martin Fergie 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 Martin Fergie. Martin Fergie 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 1
2 1
3 0
4 5
5 25
6 0
7 10
8 5
9 2
10 1
11 8
12 8
13 34
14 8
15 69
16 31
17 2
18 1
19 2
20 2

About Martin Fergie

Martin Fergie is a scholar working on Health Informatics, Statistics, Probability and Uncertainty and Dermatology, having authored 23 papers that have together received 257 indexed citations. Recurring topics across this work include AI in cancer detection (8 papers), Global Cancer Incidence and Screening (5 papers) and Ocular Surface and Contact Lens (4 papers). The work is most often cited by research in Health Informatics (37 citations), Obstetrics and Gynecology (21 citations) and Radiology, Nuclear Medicine and Imaging (56 citations). Martin Fergie has collaborated with scholars based in United Kingdom, United States and Australia. Frequent co-authors include Ignacio Atal, P. Smith, Philippe Ravaud, Michael J. Callaghan, James Selfe, Susan Astley, Richard Byers, Elaine F. Harkness, Aphrodite Galata and D. Gareth Evans. Their work appears in journals such as SHILAP Revista de lepidopterología, Blood and British Journal of Cancer.

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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