Ming Y. Lu

8.6k citations
76 papers · 4.7k indexed · 8 hit papers · h-index 33

Ming Y. Lu

70 papers receiving 4.6k citations

Hit Papers

A multimodal generative AI copilo...1562021202620222024100200300400

Peers

Ming Y. Lu
Comparison fields: 5 of 181
  • Health Informatics 462
  • Radiology, Nuclear Medicine and Imaging 1.4k
  • Ophthalmology 473
  • Artificial Intelligence 1.5k
  • Biophysics 218
Replace Yun Liu with:
Yun Liu United States
Wei Qian China
Navneet Narula United States
Marc Coram United States
David Snead United Kingdom
Jason Hipp United States
Kim Ramasamy India
Kun Huang United States
Michael Fulham Australia
Luca Saba Italy
Ming Y. Lu relative to Yun Liu United States Yun Liu's profile →
Citations per field
00.5×3.6×
Yun Liu · 1×
Citations per year

Countries citing papers authored by Ming Y. Lu

Since Specialization
Citations

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

Fields of papers citing papers by Ming Y. Lu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ming Y. Lu. 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 Ming Y. Lu. The network helps show where Ming Y. Lu may publish in the future.

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20250
2 20250
3 20243
4 20244
5 20240
6 20241
7 20233
8
Artificial intelligence for multimodal data integration in oncologybreakdown →
2022333
9 202255
10
Synthetic data in machine learning for medicine and healthcarebreakdown →
2021414
11 20212
12 202023
13 202013
14 20205
15
Orthopedics Tool Measurement Apparatus Based on three-dimensional Space Measurement
20080
16 20041
17 200157
18 199917
19 1998343
20 199434

About Ming Y. Lu

Ming Y. Lu is a scholar working on Health Informatics, Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Ophthalmology and Immunology and Allergy, having authored 76 papers that have together received 4.7k indexed citations. Recurring topics across this work include AI in cancer detection (17 papers), Radiomics and Machine Learning in Medical Imaging (10 papers), Artificial Intelligence in Healthcare and Education (6 papers), Retinal Diseases and Treatments (5 papers), Spinal Fractures and Fixation Techniques (4 papers), Orthopaedic implants and arthroplasty (4 papers), Acute Lymphoblastic Leukemia research (4 papers) and Chronic Lymphocytic Leukemia Research (3 papers). The work is most often cited by research in Health Informatics (462 citations), Radiology, Nuclear Medicine and Imaging (1.4k citations), Ophthalmology (473 citations), Artificial Intelligence (1.5k citations) and Biophysics (218 citations). Ming Y. Lu has collaborated with scholars based in United States, China and Canada. Frequent co-authors include Faisal Mahmood, Drew F. K. Williamson, Richard J. Chen, Tiffany Chen, Anthony P. Adamis, Jana Lipková, Bowen Chen, Muhammad Shaban, Karen Keough and Anurag Vaidya. Their work appears in journals such as Nature Medicine, Nature Biomedical Engineering, Nucleic Acids Research, Journal of Pathology Informatics and Diabetes.

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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