Dave Van Veen

1.2k total citations · 1 hit paper
10 papers, 356 citations indexed

About

Dave Van Veen is a scholar working on Artificial Intelligence, Molecular Biology and Health Informatics. According to data from OpenAlex, Dave Van Veen has authored 10 papers receiving a total of 356 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Artificial Intelligence, 3 papers in Molecular Biology and 2 papers in Health Informatics. Recurrent topics in Dave Van Veen's work include Topic Modeling (4 papers), Biomedical Text Mining and Ontologies (3 papers) and Natural Language Processing Techniques (3 papers). Dave Van Veen is often cited by papers focused on Topic Modeling (4 papers), Biomedical Text Mining and Ontologies (3 papers) and Natural Language Processing Techniques (3 papers). Dave Van Veen collaborates with scholars based in United States, Switzerland and Canada. Dave Van Veen's co-authors include David B. Lindell, Jeong Joon Park, Gordon Wetzstein, Akshay Chaudhari, Curtis P. Langlotz, Sergios Gatidis, Jason Hom, John M. Pauly, Eduardo Pontes Reis and Cara Van Uden and has published in prestigious journals such as Nature Medicine, Radiology and International Journal of Molecular Sciences.

In The Last Decade

Dave Van Veen

9 papers receiving 347 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Adapted large language models can outperform medical experts in clinical text summarization

2024 252 citations Dave Van Veen, Cara Van Uden et al. Nature Medicine profile →

Peers

Dave Van Veen

AI

HI

RNMI

CVPR

MB

Arpit Bansal India

William Wasswa Uganda

André Carrington Canada

Colin B. Compas United States

Mingquan Lin United States

Takuma Shibahara Japan

Jean-Benoit Delbrouck United States

Benjamin Kompa United States

Surabhi Datta United States

Kevin Faust Canada

Arpit Bansal India

Dave Van Veen

27 ×0.2

AI

40 ×0.3

HI

26 ×0.4

RNMI

42 ×0.8

CVPR

7 ×0.2

MB

Citations per year, relative to Dave Van Veen Dave Van Veen (= 1×) peers Arpit Bansal

Countries citing papers authored by Dave Van Veen

Since Specialization

Citations

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

Fields of papers citing papers by Dave Van Veen

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dave Van Veen. 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 Dave Van Veen. The network helps show where Dave Van Veen may publish in the future.

Co-authorship network of co-authors of Dave Van Veen

This figure shows the co-authorship network connecting the top 25 collaborators of Dave Van Veen. A scholar is included among the top collaborators of Dave Van Veen 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 Dave Van Veen. Dave Van Veen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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10 of 10 papers shown

1.

Zhang, Xi, Fatemeh Haghighi, Dave Van Veen, et al.. (2025). RadEval: A framework for radiology text evaluation. ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam). 546–557.

2.

Larson, David B., et al.. (2025). Assessing Completeness of Clinical Histories Accompanying Imaging Orders Using Adapted Open-Source and Closed-Source Large Language Models. Radiology. 314(2). e241051–e241051. 5 indexed citations

3.

Blüthgen, Christian, Dave Van Veen, Cyril Zakka, et al.. (2025). Best Practices for Large Language Models in Radiology. Radiology. 315(1). e240528–e240528. 8 indexed citations

4.

Delbrouck, Jean-Benoit, Pierre Chambon, Zhi Hong Chen, et al.. (2024). RadGraph-XL: A Large-Scale Expert-Annotated Dataset for Entity and Relation Extraction from Radiology Reports. 12902–12915. 1 indexed citations

5.

Veen, Dave Van, Cara Van Uden, Louis Blankemeier, et al.. (2024). Adapted large language models can outperform medical experts in clinical text summarization. Nature Medicine. 30(4). 1134–1142. 252 indexed citations breakdown →

6.

Gatti, Anthony A., Louis Blankemeier, Dave Van Veen, et al.. (2024). ShapeMed-Knee: A Dataset and Neural Shape Model Benchmark for Modeling 3D Femurs. IEEE Transactions on Medical Imaging. 44(3). 1140–1152. 1 indexed citations

7.

Aali, Asad, Dave Van Veen, Jason Hom, et al.. (2024). A dataset and benchmark for hospital course summarization with adapted large language models. Journal of the American Medical Informatics Association. 32(3). 470–479. 8 indexed citations

8.

Veen, Dave Van, Jesús G. Galaz-Montoya, Liyue Shen, et al.. (2024). Missing Wedge Completion via Unsupervised Learning with Coordinate Networks. International Journal of Molecular Sciences. 25(10). 5473–5473. 2 indexed citations

9.

Veen, Dave Van, Cara Van Uden, Anuj Pareek, et al.. (2023). RadAdapt: Radiology Report Summarization via Lightweight Domain Adaptation of Large Language Models. 449–460. 11 indexed citations

10.

Lindell, David B., Dave Van Veen, Jeong Joon Park, & Gordon Wetzstein. (2022). Bacon: Band-limited Coordinate Networks for Multiscale Scene Representation. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 16231–16241. 68 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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