Hagar Khalid

1.1k citations
37 papers · 688 · 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

Hagar Khalid

33 papers receiving 673 citations

Peers

Hagar Khalid
Comparison fields: 5 of 83
  • Ophthalmology 518
  • Health Informatics 31
  • Radiology, Nuclear Medicine and Imaging 528
  • Health Information Management 13
  • Computer Vision and Pattern Recognition 41
Replace Daniel Ferraz with:
Daniel Ferraz Brazil
Weihong Yu China
Poemen P. Chan Hong Kong
Dominika Podkowinski Austria
Ana-Maria Philip Austria
Yuri Fujino Japan
Ryan T. Yanagihara United States
Minhaj Nur Alam United States
Fangyao Tang Hong Kong
Geunyoung Lee Singapore
Hagar Khalid relative to Daniel Ferraz Brazil Daniel Ferraz's profile →
Citations per field
00.5×1.5×1.9×
Daniel Ferraz · 1×
Citations per year

Countries citing papers authored by Hagar Khalid

Since Specialization
Citations

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

Fields of papers citing papers by Hagar Khalid

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 37 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2020149
2 2021115
3 202094
4 202064
5 202049
6 201932
7 202129
8 202225
9 202022
10 201714
11 202012
12 202111
13 202011
14 20217
15 20237
16 20177
17 20207
18 20236
19 20244
20 20194

About Hagar Khalid

Hagar Khalid is a scholar working on Radiology, Nuclear Medicine and Imaging, Ophthalmology, Health Information Management, Artificial Intelligence and Molecular Biology, having authored 37 papers that have together received 688 indexed citations. Recurring topics across this work include Retinal Imaging and Analysis (26 papers), Retinal Diseases and Treatments (22 papers), Retinal and Optic Conditions (15 papers), Glaucoma and retinal disorders (9 papers), Artificial Intelligence in Healthcare (3 papers), COVID-19 diagnosis using AI (2 papers), Retinopathy of Prematurity Studies (2 papers) and Cerebral Venous Sinus Thrombosis (2 papers). The work is most often cited by research in Ophthalmology (518 citations), Health Informatics (31 citations), Radiology, Nuclear Medicine and Imaging (528 citations), Health Information Management (13 citations) and Computer Vision and Pattern Recognition (41 citations). Hagar Khalid has collaborated with scholars based in United Kingdom, Egypt and United States. Frequent co-authors include Pearse A. Keane, Konstantinos Balaskas, Siegfried K. Wagner, Livia Faes, Edward Korot, Daniel Ferraz, Josef Huemer, Dun Jack Fu, Xiaoxuan Liu and Alastair K. Denniston. Their work appears in journals such as Investigative Ophthalmology & Visual Science, British Journal of Ophthalmology, Eye, American Journal of Ophthalmology and JAMA Ophthalmology.

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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