Fang-I Lu
Impact in
- Health Informatics top 10%
- Artificial Intelligence in Healthcare and Education
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- Ovarian cancer diagnosis and treatment
- Endometriosis Research and Treatment
Papers in
-
- Ovarian cancer diagnosis and treatment 3
- Endometriosis Research and Treatment 1
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- Endometrial and Cervical Cancer Treatments 2
- Co-authors
- Katarzyna J. Jerzak (1 shared paper)Andrew Lagree (1 shared paper)Jonathan Klein (1 shared paper)Ali Sadeghi‐Naini (1 shared paper)Tina Wu (1 shared paper)William T. Tran (1 shared paper)Sami Tabbarah (1 shared paper)Iván M. Rosado-Méndez (1 shared paper)
- Journals
- International Journal of Gynecological Pathology (2 papers)Archives of Pathology & Laboratory Medicine (1 paper)International Journal of Surgical Pathology (1 paper)Journal of medical imaging and radiation sciences (1 paper)
- Partner nations
- CanadaEgyptUnited States
In The Last Decade
Fang-I Lu
5 papers receiving 107 citations
Peers
Comparison fields: 5 of 29
- Health Informatics 14
- Reproductive Medicine 35
- Obstetrics and Gynecology 16
- Radiology, Nuclear Medicine and Imaging 35
- Cancer Research 11
Countries citing papers authored by Fang-I Lu
This map shows the geographic impact of Fang-I 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 Fang-I Lu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fang-I Lu more than expected).
Fields of papers citing papers by Fang-I Lu
This network shows the impact of papers produced by Fang-I 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 Fang-I Lu. The network helps show where Fang-I Lu may publish in the future.
Co-authors
The 21 scholars most cited alongside Fang-I Lu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 65 | |
| 2 | 2018 | 24 | |
| 3 | 2018 | 10 | |
| 4 | 2015 | 7 | |
| 5 | 2025 | 1 |
About Fang-I Lu
Fang-I Lu is a scholar working on Reproductive Medicine, Obstetrics and Gynecology, Oncology, Epidemiology and Artificial Intelligence, having authored 5 papers that have together received 107 indexed citations. Recurring topics across this work include Ovarian cancer diagnosis and treatment (3 papers), Endometrial and Cervical Cancer Treatments (2 papers), Radiomics and Machine Learning in Medical Imaging (1 paper), Endometriosis Research and Treatment (1 paper), Galectins and Cancer Biology (1 paper), Gastrointestinal Tumor Research and Treatment (1 paper), Medical Imaging Techniques and Applications (1 paper) and AI in cancer detection (1 paper). The work is most often cited by research in Health Informatics (14 citations), Reproductive Medicine (35 citations), Obstetrics and Gynecology (16 citations), Radiology, Nuclear Medicine and Imaging (35 citations) and Cancer Research (11 citations). Fang-I Lu has collaborated with scholars based in Canada, Egypt and United States. Frequent co-authors include Katarzyna J. Jerzak, Andrew Lagree, Jonathan Klein, Ali Sadeghi‐Naini, Tina Wu, William T. Tran, Sami Tabbarah, Iván M. Rosado-Méndez, Elzbieta Slodkowska and Valérie Dubé. Their work appears in journals such as International Journal of Gynecological Pathology, Archives of Pathology & Laboratory Medicine, International Journal of Surgical Pathology and Journal of medical imaging and radiation sciences.
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.