Joy T. Wu

831 citations
23 papers · 460 · h-index 11

Impact in

Papers in

Joy T. Wu

21 papers receiving 437 citations

Peers

Joy T. Wu
Comparison fields: 5 of 100
  • Health Informatics 77
  • Health Information Management 43
  • Radiology, Nuclear Medicine and Imaging 142
  • Artificial Intelligence 172
  • Family Practice 4
Replace Lin Guo with:
Lin Guo China
Sharifa Sahai United States
Ramsey M. Wehbe United States
Kenji Ikemura United States
Kevin Faust Canada
Colin B. Compas United States
Zirun Zhao United States
Alanna Vial Australia
Malgorzata Polacin Switzerland
Joy T. Wu relative to Lin Guo China Lin Guo's profile →
Citations per field
00.5×3.5×
Lin Guo · 1×
Citations per year

Countries citing papers authored by Joy T. Wu

Since Specialization
Citations

This map shows the geographic impact of Joy T. Wu'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 Joy T. Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Joy T. Wu more than expected).

Fields of papers citing papers by Joy T. Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Joy T. Wu. 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 Joy T. Wu. The network helps show where Joy T. Wu may publish in the future.

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 23 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2018149
2 202094
3 197644
4 201726
5 201923
6 202021
7 201015
8 202014
9 202113
10 202310
11 201910
12 20229
13 20209
14
AI Accelerated Human-in-the-loop Structuring of Radiology Reports.
20208
15
Combining Deep Learning and Knowledge-driven Reasoning for Chest X-Ray Findings Detection.
20204
16
Multimodal Pediatric Lymphoma Detection using PET and MRI.
20234
17 20172
18 20182
19
Semantic Expansion of Clinician Generated Data Preferences for Automatic Patient Data Summarization.
20211
20 20191

About Joy T. Wu

Joy T. Wu is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Molecular Biology, Health Informatics and Health Information Management, having authored 23 papers that have together received 460 indexed citations. Recurring topics across this work include COVID-19 diagnosis using AI (9 papers), Radiomics and Machine Learning in Medical Imaging (7 papers), Machine Learning in Healthcare (6 papers), AI in cancer detection (5 papers), Topic Modeling (4 papers), Artificial Intelligence in Healthcare and Education (4 papers), Biomedical Text Mining and Ontologies (4 papers) and Radiology practices and education (3 papers). The work is most often cited by research in Health Informatics (77 citations), Health Information Management (43 citations), Radiology, Nuclear Medicine and Imaging (142 citations), Artificial Intelligence (172 citations) and Family Practice (4 citations). Joy T. Wu has collaborated with scholars based in United States, Germany and New Zealand. Frequent co-authors include Leo Anthony Celi, Tanveer Syeda-Mahmood, Patrick D. Tyler, Franck Dernoncourt, Mehdi Moradi, Sebastian Gehrmann, Edward T. Moseley, Eric T. Carlson, Yeran Li and Jonathan Welt. Their work appears in journals such as Journal of Digital Imaging, Radiographics, IEEE Transactions on Electron Devices, PLoS ONE and Radiology Artificial Intelligence.

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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