Sunyang Fu

2.5k citations
92 papers · 1.3k · 1 hit paper · h-index 18

Impact in

Papers in

Sunyang Fu

79 papers receiving 1.2k citations

Sunyang Fu's Hit Papers

A framework for human evaluation of large language models in healthcare derived from literature review 2024 · 102 citations
1020+1Years since publication255075100

Peers

Sunyang Fu
Comparison fields: 5 of 106
  • Health Informatics 216
  • Health Information Management 98
  • Issues, ethics and legal aspects 14
  • Artificial Intelligence 406
  • Critical Care and Intensive Care Medicine 31
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Andrew Wen United States
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Citations per year

Countries citing papers authored by Sunyang Fu

Since Specialization
Citations

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

Fields of papers citing papers by Sunyang Fu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 92 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2020123
2
A framework for human evaluation of large language models in healthcare derived from literature review
Hit paper breakdown →
2024102
3 2019101
4 201887
5 201957
6 201945
7 202042
8 201939
9 202036
10 202035
11 202234
12 202033
13 202031
14 202229
15 202227
16 202124
17 202122
18 201820
19 202217
20 201516

About Sunyang Fu

Sunyang Fu is a scholar working on Artificial Intelligence, Molecular Biology, Health Information Management, Epidemiology and Psychiatry and Mental health, having authored 92 papers that have together received 1.3k indexed citations. Recurring topics across this work include Biomedical Text Mining and Ontologies (19 papers), Machine Learning in Healthcare (12 papers), Topic Modeling (11 papers), Electronic Health Records Systems (7 papers), Acute Ischemic Stroke Management (7 papers), Dementia and Cognitive Impairment Research (7 papers), Natural Language Processing Techniques (5 papers) and Artificial Intelligence in Healthcare (4 papers). The work is most often cited by research in Health Informatics (216 citations), Health Information Management (98 citations), Issues, ethics and legal aspects (14 citations), Artificial Intelligence (406 citations) and Critical Care and Intensive Care Medicine (31 citations). Sunyang Fu has collaborated with scholars based in United States, United Kingdom and Germany. Frequent co-authors include Hongfang Liu, Sunghwan Sohn, Yanshan Wang, Andrew Wen, Liwei Wang, Feichen Shen, Sijia Liu, Sungrim Moon, Hilal Maradit Kremers and David G. Lewallen. Their work appears in journals such as npj Digital Medicine, The Journal of Arthroplasty, Journal of the American Medical Informatics Association, Journal of Biomedical Informatics and The Journals of Gerontology Series A.

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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