Kim L. Sandler
- Otorhinolaryngology top 5%
-
- Lung Cancer Diagnosis and Treatment 35
- Lung Cancer Treatments and Mutations 8
- Pleural and Pulmonary Diseases 5
- Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis 4
- Health Informatics top 10%
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- Radiomics and Machine Learning in Medical Imaging 22
- COVID-19 diagnosis using AI 6
- Medical Imaging Techniques and Applications 5
- Oncology top 10%
- Global Cancer Incidence and Screening 13
- Co-authors
- Melinda C. AldrichEric L. GroganDiane N. HaddadSarah MercaldoWilliam J. BlotJeffrey D. BlumeM. Patricia RiveraLouise M. Henderson
- Journals
- Journal of the American College of Radiology (9 papers)Radiology Artificial Intelligence (2 papers)Journal of Thoracic Oncology (2 papers)
- Partner nations
- United StatesIndiaFrance
In The Last Decade
Kim L. Sandler
46 papers receiving 810 citations
Peers
Comparison fields: 5 of 82
- Otorhinolaryngology 81
- Pulmonary and Respiratory Medicine 566
- Health Informatics 18
- Radiology, Nuclear Medicine and Imaging 253
- Oncology 262
Countries citing papers authored by Kim L. Sandler
This map shows the geographic impact of Kim L. Sandler'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 Kim L. Sandler with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kim L. Sandler more than expected).
Fields of papers citing papers by Kim L. Sandler
This network shows the impact of papers produced by Kim L. Sandler. 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 Kim L. Sandler. The network helps show where Kim L. Sandler may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Kim L. Sandler, 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 | 2025 | 0 | |
| 2 | 2024 | 6 | |
| 3 | 2024 | 2 | |
| 4 | 2024 | 1 | |
| 5 | 2023 | 33 | |
| 6 | 2023 | 11 | |
| 7 | 2023 | 8 | |
| 8 | 2023 | 9 | |
| 9 | 2023 | 0 | |
| 10 | 2023 | 9 | |
| 11 | 2022 | 7 | |
| 12 | 2022 | 6 | |
| 13 | 2021 | 11 | |
| 14 | 2021 | 12 | |
| 15 | 2021 | 6 | |
| 16 | 2020 | 128 | |
| 17 | 2018 | 5 | |
| 18 | 2017 | 4 | |
| 19 | 2017 | 38 | |
| 20 | 2016 | 0 |
About Kim L. Sandler
Kim L. Sandler is a scholar working on Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging and Otorhinolaryngology, having authored 56 papers that have together received 832 indexed citations. Recurring topics across this work include Lung Cancer Diagnosis and Treatment (35 papers), Radiomics and Machine Learning in Medical Imaging (22 papers), Global Cancer Incidence and Screening (13 papers), Lung Cancer Treatments and Mutations (8 papers), COVID-19 diagnosis using AI (6 papers), Medical Imaging Techniques and Applications (5 papers), Pleural and Pulmonary Diseases (5 papers) and Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis (4 papers). The work is most often cited by research in Otorhinolaryngology (81 citations), Pulmonary and Respiratory Medicine (566 citations) and Health Informatics (18 citations). Kim L. Sandler has collaborated with scholars based in United States, India and France. Frequent co-authors include Melinda C. Aldrich, Eric L. Grogan, Diane N. Haddad, Sarah Mercaldo, William J. Blot, Jeffrey D. Blume, M. Patricia Rivera, Louise M. Henderson, Pierre P. Massion and Bennett A. Landman. Their work appears in journals such as Journal of the American College of Radiology, Radiology Artificial Intelligence, Journal of Thoracic Oncology, Medical Image Analysis and Cancer Biomarkers.
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.