William Lotter
Impact in
- Health Informatics top 1%
- Artificial Intelligence in Healthcare and Education
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- Radiomics and Machine Learning in Medical Imaging
Papers in
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- AI in cancer detection 7
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- Radiomics and Machine Learning in Medical Imaging 5
- Co-authors
- David Cox (2 shared papers)Gabriel Kreiman (2 shared papers)Abdul Rahman Diab (4 shared papers)Jiye G. Kim (3 shared papers)Giorgia Grisot (2 shared papers)Bryan Haslam (2 shared papers)Jerrold L. Boxerman (1 shared paper)Kevin Wu (1 shared paper)
- Journals
- Radiology Artificial Intelligence (3 papers)npj Digital Medicine (2 papers)Nature Communications (1 paper)JCO Clinical Cancer Informatics (1 paper)BMJ Open (1 paper)
- Partner nations
- United StatesAustraliaSweden
In The Last Decade
William Lotter
20 papers receiving 732 citations
William Lotter's Hit Papers
Peers
Comparison fields: 5 of 105
- Health Informatics 105
- Radiology, Nuclear Medicine and Imaging 218
- Artificial Intelligence 304
- Cognitive Neuroscience 127
- Oncology 80
Countries citing papers authored by William Lotter
This map shows the geographic impact of William Lotter'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 William Lotter with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites William Lotter more than expected).
Fields of papers citing papers by William Lotter
This network shows the impact of papers produced by William Lotter. 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 William Lotter. The network helps show where William Lotter may publish in the future.
Co-authors
The 25 scholars most cited alongside William Lotter, 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 | Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach. Hit paper breakdown → | 2021 | 255 |
| 2 | 2018 | 110 | |
| 3 | Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions Hit paper breakdown → | 2024 | 87 |
| 4 | 2020 | 58 | |
| 5 | 2023 | 52 | |
| 6 | 2024 | 45 | |
| 7 | 2022 | 31 | |
| 8 | 2024 | 26 | |
| 9 | 2022 | 22 | |
| 10 | 2022 | 15 | |
| 11 | 2025 | 14 | |
| 12 | 2024 | 9 | |
| 13 | 2024 | 4 | |
| 14 | 2024 | 3 | |
| 15 | 2025 | 3 | |
| 16 | 2024 | 2 | |
| 17 | 2023 | 1 | |
| 18 | 2025 | 1 | |
| 19 | 2025 | 1 | |
| 20 | 2025 | 1 |
About William Lotter
William Lotter is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Oncology, Pulmonary and Respiratory Medicine and Health Informatics, having authored 20 papers that have together received 740 indexed citations. Recurring topics across this work include AI in cancer detection (7 papers), Radiomics and Machine Learning in Medical Imaging (5 papers), Digital Radiography and Breast Imaging (3 papers), Artificial Intelligence in Healthcare and Education (3 papers), Cancer Immunotherapy and Biomarkers (3 papers), Neural dynamics and brain function (2 papers), Global Cancer Incidence and Screening (2 papers) and Nanoplatforms for cancer theranostics (2 papers). The work is most often cited by research in Health Informatics (105 citations), Radiology, Nuclear Medicine and Imaging (218 citations), Artificial Intelligence (304 citations), Cognitive Neuroscience (127 citations) and Oncology (80 citations). William Lotter has collaborated with scholars based in United States, Australia and Sweden. Frequent co-authors include David Cox, Gabriel Kreiman, Abdul Rahman Diab, Jiye G. Kim, Giorgia Grisot, Bryan Haslam, Jerrold L. Boxerman, Kevin Wu, Eric Q. Wu and Gopal R. Vijayaraghavan. Their work appears in journals such as Radiology Artificial Intelligence, npj Digital Medicine, Nature Communications, JCO Clinical Cancer Informatics and BMJ Open.
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.