Pranav Rajpurkar
- Health Informatics top 0.01%
- Artificial Intelligence in Healthcare and Education 30
- Artificial Intelligence top 0.1%
- AI in cancer detection 12
- Topic Modeling 11
- Machine Learning in Healthcare 9
- Health Information Management top 0.2%
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- Multimodal Machine Learning Applications 5
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- Radiomics and Machine Learning in Medical Imaging 24
- COVID-19 diagnosis using AI 16
- Radiology practices and education 6
- Co-authors
- Percy LiangJian ZhangEric J. TopolOishi BanerjeeAndrew Y. NgEmma ChenRobin JiaMasoumeh Haghpanahi
- Partner nations
- United StatesCanadaVietnam
In The Last Decade
Pranav Rajpurkar
71 papers receiving 10.3k citations
Hit Papers
Peers
Comparison fields: 5 of 203
- Health Informatics 1.8k
- Artificial Intelligence 5.8k
- Health Information Management 458
- Computer Vision and Pattern Recognition 1.8k
- Radiology, Nuclear Medicine and Imaging 1.9k
Countries citing papers authored by Pranav Rajpurkar
This map shows the geographic impact of Pranav Rajpurkar'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 Pranav Rajpurkar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pranav Rajpurkar more than expected).
Fields of papers citing papers by Pranav Rajpurkar
This network shows the impact of papers produced by Pranav Rajpurkar. 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 Pranav Rajpurkar. The network helps show where Pranav Rajpurkar may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Pranav Rajpurkar, 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 | 14 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 0 | |
| 4 | 2024 | 3 | |
| 5 | 2023 | 4 | |
| 6 | 2023 | 57 | |
| 7 | 2023 | 16 | |
| 8 | Foundation models for generalist medical artificial intelligencebreakdown → | 2023 | 738 |
| 9 | 2023 | 14 | |
| 10 | 2023 | 7 | |
| 11 | 2023 | 21 | |
| 12 | 2022 | 107 | |
| 13 | Self-supervised learning in medicine and healthcarebreakdown → | 2022 | 326 |
| 14 | Multimodal biomedical AIbreakdown → | 2022 | 561 |
| 15 | 2021 | 26 | |
| 16 | MoCo-Pretraining Improves Representations and Transferability of Chest X-ray Models | 2021 | 5 |
| 17 | 2020 | 103 | |
| 18 | 2020 | 89 | |
| 19 | 2020 | 37 | |
| 20 | 2019 | 140 |
About Pranav Rajpurkar
Pranav Rajpurkar is a scholar working on Health Informatics, Radiology, Nuclear Medicine and Imaging and Issues, ethics and legal aspects, having authored 76 papers that have together received 10.8k indexed citations. Recurring topics across this work include Artificial Intelligence in Healthcare and Education (30 papers), Radiomics and Machine Learning in Medical Imaging (24 papers), COVID-19 diagnosis using AI (16 papers), AI in cancer detection (12 papers), Topic Modeling (11 papers), Machine Learning in Healthcare (9 papers), Radiology practices and education (6 papers) and Multimodal Machine Learning Applications (5 papers). The work is most often cited by research in Health Informatics (1.8k citations), Artificial Intelligence (5.8k citations) and Health Information Management (458 citations). Pranav Rajpurkar has collaborated with scholars based in United States, Canada and Vietnam. Frequent co-authors include Percy Liang, Jian Zhang, Eric J. Topol, Oishi Banerjee, Andrew Y. Ng, Emma Chen, Robin Jia, Masoumeh Haghpanahi, Awni Hannun and Geoffrey H. Tison. Their work appears in journals such as Nature, New England Journal of Medicine and Cell.
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.