Winston Lie
Impact in
- Health Informatics top 0.2%
- Artificial Intelligence in Healthcare and Education
- Family Practice top 5%
- Clinical Reasoning and Diagnostic Skills
Papers in
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- Artificial Intelligence in Healthcare and Education 2
-
- COVID-19 diagnosis using AI 1
- Radiomics and Machine Learning in Medical Imaging 1
- Co-authors
- Marc D. Succi (5 shared papers)Arya Rao (4 shared papers)Michael Pang (3 shared papers)John Kim (4 shared papers)Keith J. Dreyer (3 shared papers)Meghana Kamineni (2 shared papers)Adam Landman (2 shared papers)A Prasad (1 shared paper)
- Journals
- Journal of Medical Internet Research (1 paper)Journal of the American Pharmacists Association (1 paper)Journal of the American College of Radiology (1 paper)American Journal of Neuroradiology (1 paper)Journal of Medical Systems (1 paper)
- Partner nations
- United States
In The Last Decade
Winston Lie
5 papers receiving 437 citations
Winston Lie's Hit Papers
Peers
Comparison fields: 5 of 60
- Health Informatics 357
- Family Practice 51
- Radiology, Nuclear Medicine and Imaging 212
- Artificial Intelligence 181
- Health Information Management 24
Countries citing papers authored by Winston Lie
This map shows the geographic impact of Winston Lie'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 Winston Lie with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Winston Lie more than expected).
Fields of papers citing papers by Winston Lie
This network shows the impact of papers produced by Winston Lie. 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 Winston Lie. The network helps show where Winston Lie may publish in the future.
Co-authors
The 15 scholars most cited alongside Winston Lie, 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 | Assessing the Utility of ChatGPT Throughout the Entire Clinical Workflow: Development and Usability Study Hit paper breakdown → | 2023 | 228 |
| 2 | 2023 | 189 | |
| 3 | 2024 | 18 | |
| 4 | 2024 | 4 | |
| 5 | 2024 | 1 |
About Winston Lie
Winston Lie is a scholar working on Health Informatics, Radiology, Nuclear Medicine and Imaging, Surgery, Cardiology and Cardiovascular Medicine and Geriatrics and Gerontology, having authored 5 papers that have together received 440 indexed citations. Recurring topics across this work include Artificial Intelligence in Healthcare and Education (2 papers), Cardiac, Anesthesia and Surgical Outcomes (1 paper), Pelvic and Acetabular Injuries (1 paper), COVID-19 diagnosis using AI (1 paper), Pharmaceutical Practices and Patient Outcomes (1 paper), Computational Drug Discovery Methods (1 paper), Radiomics and Machine Learning in Medical Imaging (1 paper) and Heart Failure Treatment and Management (1 paper). The work is most often cited by research in Health Informatics (357 citations), Family Practice (51 citations), Radiology, Nuclear Medicine and Imaging (212 citations), Artificial Intelligence (181 citations) and Health Information Management (24 citations). Winston Lie has collaborated with scholars based in United States. Frequent co-authors include Marc D. Succi, Arya Rao, Michael Pang, John Kim, Keith J. Dreyer, Meghana Kamineni, Adam Landman, A Prasad, Karen Buch and Alexander H. King. Their work appears in journals such as Journal of Medical Internet Research, Journal of the American Pharmacists Association, Journal of the American College of Radiology, American Journal of Neuroradiology and Journal of Medical Systems.
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.