Louis Blankemeier
Impact in
- Health Informatics top 1%
- Artificial Intelligence in Healthcare and Education
Papers in
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- Nanowire Synthesis and Applications 2
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- Radiology practices and education 2
- Radiomics and Machine Learning in Medical Imaging 2
- Co-authors
- Rehan Kapadia (5 shared papers)Shanyuan Niu (3 shared papers)Matthew Yeung (3 shared papers)Debarghya Sarkar (4 shared papers)Jayakanth Ravichandran (3 shared papers)Akshay Chaudhari (10 shared papers)Curtis P. Langlotz (4 shared papers)Kevin Ye (1 shared paper)
- Journals
- ACS Nano (2 papers)Advanced Materials (2 papers)Nature Medicine (1 paper)Radiology (1 paper)IEEE Transactions on Medical Imaging (1 paper)
- Partner nations
- United StatesCanadaBrazil
In The Last Decade
Louis Blankemeier
17 papers receiving 810 citations
Louis Blankemeier's Hit Papers
Peers
Comparison fields: 5 of 108
- Health Informatics 122
- Family Practice 12
- Materials Chemistry 244
- Surfaces, Coatings and Films 35
- Electrical and Electronic Engineering 285
Countries citing papers authored by Louis Blankemeier
This map shows the geographic impact of Louis Blankemeier'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 Louis Blankemeier with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Louis Blankemeier more than expected).
Fields of papers citing papers by Louis Blankemeier
This network shows the impact of papers produced by Louis Blankemeier. 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 Louis Blankemeier. The network helps show where Louis Blankemeier may publish in the future.
Co-authors
The 25 scholars most cited alongside Louis Blankemeier, 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 | Adapted large language models can outperform medical experts in clinical text summarization Hit paper breakdown → | 2024 | 324 |
| 2 | 2016 | 232 | |
| 3 | 2022 | 149 | |
| 4 | 2017 | 30 | |
| 5 | 2025 | 28 | |
| 6 | 2018 | 15 | |
| 7 | 2024 | 13 | |
| 8 | 2024 | 8 | |
| 9 | 2023 | 7 | |
| 10 | 2024 | 6 | |
| 11 | 2017 | 6 | |
| 12 | 2019 | 5 | |
| 13 | 2024 | 4 | |
| 14 | 2024 | 1 | |
| 15 | 2024 | 1 | |
| 16 | 2024 | 1 | |
| 17 | 2022 | 1 |
About Louis Blankemeier
Louis Blankemeier is a scholar working on Biomedical Engineering, Radiology, Nuclear Medicine and Imaging, Materials Chemistry, Artificial Intelligence and Physiology, having authored 17 papers that have together received 831 indexed citations. Recurring topics across this work include Nutrition and Health in Aging (3 papers), Body Composition Measurement Techniques (2 papers), Machine Learning in Healthcare (2 papers), Radiology practices and education (2 papers), Quantum Dots Synthesis And Properties (2 papers), Semiconductor materials and devices (2 papers), Radiomics and Machine Learning in Medical Imaging (2 papers) and Nanowire Synthesis and Applications (2 papers). The work is most often cited by research in Health Informatics (122 citations), Family Practice (12 citations), Materials Chemistry (244 citations), Surfaces, Coatings and Films (35 citations) and Electrical and Electronic Engineering (285 citations). Louis Blankemeier has collaborated with scholars based in United States, Canada and Brazil. Frequent co-authors include Rehan Kapadia, Shanyuan Niu, Matthew Yeung, Debarghya Sarkar, Jayakanth Ravichandran, Akshay Chaudhari, Curtis P. Langlotz, Kevin Ye, Thomas Orvis and David J. Singh. Their work appears in journals such as ACS Nano, Advanced Materials, Nature Medicine, Radiology and IEEE Transactions on Medical Imaging.
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.