Jared Dunnmon

2.1k total citations
23 papers, 908 citations indexed

About

Jared Dunnmon is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Health Informatics. According to data from OpenAlex, Jared Dunnmon has authored 23 papers receiving a total of 908 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Radiology, Nuclear Medicine and Imaging, 7 papers in Artificial Intelligence and 4 papers in Health Informatics. Recurrent topics in Jared Dunnmon's work include Radiomics and Machine Learning in Medical Imaging (5 papers), Artificial Intelligence in Healthcare and Education (4 papers) and COVID-19 diagnosis using AI (3 papers). Jared Dunnmon is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (5 papers), Artificial Intelligence in Healthcare and Education (4 papers) and COVID-19 diagnosis using AI (3 papers). Jared Dunnmon collaborates with scholars based in United States, Sweden and Canada. Jared Dunnmon's co-authors include Christopher Ré, Samuel C. Stanton, Brian P. Mann, Earl H. Dowell, Matthew P. Lungren, Daniel L. Rubin, Curtis P. Langlotz, Darvin Yi, Alexander Ratner and Braden Hancock and has published in prestigious journals such as Nature Communications, Radiology and Proceedings of the Combustion Institute.

In The Last Decade

Jared Dunnmon

23 papers receiving 890 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Jared Dunnmon United States 14 284 282 150 143 128 23 908
Pulkit Agrawal United States 14 289 1.0× 372 1.3× 231 1.5× 48 0.3× 53 0.4× 46 1.4k
Lucian Itu Romania 16 597 2.1× 127 0.5× 203 1.4× 67 0.5× 19 0.1× 76 1.1k
Jianhuang Wu China 21 379 1.3× 162 0.6× 231 1.5× 134 0.9× 34 0.3× 74 1.3k
Daniel Jarrett United Kingdom 11 148 0.5× 275 1.0× 146 1.0× 156 1.1× 21 0.2× 27 1.0k
Martin Lillholm Denmark 17 479 1.7× 572 2.0× 170 1.1× 54 0.4× 45 0.4× 49 1.4k
Qianwei Zhou China 17 264 0.9× 385 1.4× 122 0.8× 24 0.2× 29 0.2× 62 1.1k
David Harmon United States 17 73 0.3× 41 0.1× 94 0.6× 651 4.6× 101 0.8× 62 1.4k
Jingfan Fan China 19 605 2.1× 240 0.9× 365 2.4× 62 0.4× 17 0.1× 124 1.5k
Danni Ai China 19 501 1.8× 185 0.7× 392 2.6× 79 0.6× 25 0.2× 154 1.4k

Countries citing papers authored by Jared Dunnmon

Since Specialization
Citations

This map shows the geographic impact of Jared Dunnmon'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 Jared Dunnmon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jared Dunnmon more than expected).

Fields of papers citing papers by Jared Dunnmon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jared Dunnmon. 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 Jared Dunnmon. The network helps show where Jared Dunnmon may publish in the future.

Co-authorship network of co-authors of Jared Dunnmon

This figure shows the co-authorship network connecting the top 25 collaborators of Jared Dunnmon. A scholar is included among the top collaborators of Jared Dunnmon based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jared Dunnmon. Jared Dunnmon is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Zhang, Yanbo, A. K. M. Firoj Mahmud, Alexander Lavin, et al.. (2025). Exploring the role of large language models in the scientific method: from hypothesis to discovery. ePrints Soton (University of Southampton). 1(1). 1 indexed citations
2.
Tang, Siyi, Amara Tariq, Jared Dunnmon, et al.. (2023). Predicting 30-day all-cause hospital readmission using multimodal spatiotemporal graph neural networks. IEEE Journal of Biomedical and Health Informatics. 27(4). 1–12. 17 indexed citations
3.
Thompson, Elaine E., Jared Dunnmon, Arash Mohtashamian, et al.. (2022). Independent assessment of a deep learning system for lymph node metastasis detection on the Augmented Reality Microscope. Journal of Pathology Informatics. 13. 100142–100142. 2 indexed citations
4.
Saab, Khaled, Maya Varma, Pierre Chambon, et al.. (2022). ViLMedic: a framework for research at the intersection of vision and language in medical AI. 23–34. 21 indexed citations
5.
Hooper, Sarah, Jared Dunnmon, Matthew P. Lungren, et al.. (2021). Impact of Upstream Medical Image Processing on Downstream Performance of a Head CT Triage Neural Network. Radiology Artificial Intelligence. 3(4). e200229–e200229. 7 indexed citations
6.
Dunnmon, Jared. (2021). Separating Hope from Hype. Radiologic Clinics of North America. 59(6). 1063–1074. 7 indexed citations
7.
Patel, Bhavik N., et al.. (2021). Multi-task weak supervision enables anatomically-resolved abnormality detection in whole-body FDG-PET/CT. Nature Communications. 12(1). 1880–1880. 25 indexed citations
8.
Huang, Shih-Cheng, Imon Banerjee, Chris Chute, et al.. (2020). PENet—a scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric CT imaging. npj Digital Medicine. 3(1). 61–61. 103 indexed citations
9.
Dunnmon, Jared, et al.. (2020). Comparison of segmentation-free and segmentation-dependent computer-aided diagnosis of breast masses on a public mammography dataset. Journal of Biomedical Informatics. 113. 103656–103656. 11 indexed citations
10.
Saab, Khaled, Jared Dunnmon, Christopher Ré, Daniel L. Rubin, & Christopher Lee‐Messer. (2020). Weak supervision as an efficient approach for automated seizure detection in electroencephalography. npj Digital Medicine. 3(1). 59–59. 47 indexed citations
11.
Sohoni, Nimit S., et al.. (2020). No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained Classification Problems. arXiv (Cornell University). 33. 19339–19352. 20 indexed citations
12.
Fries, Jason, Paroma Varma, Ke Xiao, et al.. (2019). Weakly supervised classification of aortic valve malformations using unlabeled cardiac MRI sequences. Nature Communications. 10(1). 3111–3111. 56 indexed citations
13.
Ratner, Alexander, et al.. (2019). Training Complex Models with Multi-Task Weak Supervision. Proceedings of the AAAI Conference on Artificial Intelligence. 33(1). 4763–4771. 76 indexed citations
14.
Ratner, Alex, Braden Hancock, Jared Dunnmon, Roger E. Goldman, & Christopher Ré. (2018). Snorkel MeTaL. PubMed. 2018. 1–4. 22 indexed citations
15.
Dunnmon, Jared, Darvin Yi, Curtis P. Langlotz, et al.. (2018). Assessment of Convolutional Neural Networks for Automated Classification of Chest Radiographs. Radiology. 290(2). 537–544. 150 indexed citations
16.
17.
Ratner, Alexander, Henry R. Ehrenberg, Zeshan Hussain, Jared Dunnmon, & Cristina Re. (2017). Learning to Compose Domain-Specific Transformations for Data Augmentation.. PubMed. 30. 3239–3249. 35 indexed citations
18.
Dunnmon, Jared, Sadaf Sobhani, Tae Wook Kim, Anthony R. Kovscek, & Matthias Ihme. (2015). Characterization of scalar mixing in dense gaseous jets using X-ray computed tomography. Experiments in Fluids. 56(10). 5 indexed citations
19.
Dunnmon, Jared, Samuel C. Stanton, Brian P. Mann, & Earl H. Dowell. (2011). Power extraction from aeroelastic limit cycle oscillations. Journal of Fluids and Structures. 27(8). 1182–1198. 183 indexed citations
20.
Dunnmon, Jared, et al.. (2010). Renewable Energy Use Advantages of Maglev-Based Personal Rapid Transit. Transportation Research Record Journal of the Transportation Research Board. 2146(1). 69–75. 2 indexed citations

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.

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