Daniel Ferraz

1.2k citations
43 papers · 716 · h-index 12

Impact in

    • Retinal Diseases and Treatments
    • Retinal and Optic Conditions
    • Glaucoma and retinal disorders
    • Artificial Intelligence in Healthcare and Education

Papers in

Daniel Ferraz

38 papers receiving 700 citations

Peers

Daniel Ferraz
Comparison fields: 5 of 85
  • Ophthalmology 340
  • Health Informatics 34
  • Radiology, Nuclear Medicine and Imaging 330
  • Health Information Management 25
  • Neurology 31
Replace Albert T.A. Liem with:
Albert T.A. Liem Netherlands
Hagar Khalid United Kingdom
T. Y. Alvin Liu United States
Abigail E Huang United States
Weihong Yu China
Siddharth Nath Canada
Hironobu Tampo Japan
Dominika Podkowinski Austria
Zaid Mammo Canada
Daniel Ferraz relative to Albert T.A. Liem Netherlands Albert T.A. Liem's profile →
Citations per field
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Albert T.A. Liem · 1×
Citations per year

Countries citing papers authored by Daniel Ferraz

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Ferraz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 43 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2020171
2 2021130
3 2020101
4 202151
5 201435
6 201435
7 201333
8 201324
9 201414
10 201414
11 202313
12 202412
13 20179
14 20238
15 20217
16 20227
17 20236
18 20106
19 20195
20 20214

About Daniel Ferraz

Daniel Ferraz is a scholar working on Ophthalmology, Radiology, Nuclear Medicine and Imaging, Health Informatics, Molecular Biology and Genetics, having authored 43 papers that have together received 716 indexed citations. Recurring topics across this work include Retinal Diseases and Treatments (18 papers), Retinal Imaging and Analysis (15 papers), Retinal and Optic Conditions (10 papers), Artificial Intelligence in Healthcare and Education (5 papers), Retinal Development and Disorders (3 papers), Ocular Diseases and Behçet’s Syndrome (3 papers), COVID-19 diagnosis using AI (3 papers) and Artificial Intelligence in Healthcare (2 papers). The work is most often cited by research in Ophthalmology (340 citations), Health Informatics (34 citations), Radiology, Nuclear Medicine and Imaging (330 citations), Health Information Management (25 citations) and Neurology (31 citations). Daniel Ferraz has collaborated with scholars based in Brazil, United States and United Kingdom. Frequent co-authors include Edward Korot, Pearse A. Keane, Hagar Khalid, Siegfried K. Wagner, Livia Faes, Xiaoxuan Liu, Alastair K. Denniston, Dun Jack Fu, Christopher Kelly and Quan Dong Nguyen. Their work appears in journals such as Investigative Ophthalmology & Visual Science, International Journal of Retina and Vitreous, Eye, Journal of Ophthalmic Inflammation and Infection and Diabetes Research and Clinical Practice.

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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