Jean-Benoit Delbrouck

1.2k total citations · 1 hit paper
13 papers, 359 citations indexed

About

Jean-Benoit Delbrouck is a scholar working on Artificial Intelligence, Molecular Biology and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Jean-Benoit Delbrouck has authored 13 papers receiving a total of 359 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 7 papers in Molecular Biology and 4 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Jean-Benoit Delbrouck's work include Topic Modeling (8 papers), Biomedical Text Mining and Ontologies (7 papers) and Natural Language Processing Techniques (4 papers). Jean-Benoit Delbrouck is often cited by papers focused on Topic Modeling (8 papers), Biomedical Text Mining and Ontologies (7 papers) and Natural Language Processing Techniques (4 papers). Jean-Benoit Delbrouck collaborates with scholars based in United States, Canada and Switzerland. Jean-Benoit Delbrouck's co-authors include Curtis P. Langlotz, Christian Bluethgen, Akshay Chaudhari, John M. Pauly, Louis Blankemeier, Cara Van Uden, Malgorzata Polacin, Anuj Pareek, Dave Van Veen and Poonam Hosamani and has published in prestigious journals such as Nature Medicine, Nature Biomedical Engineering and Journal of the American College of Radiology.

In The Last Decade

Jean-Benoit Delbrouck

11 papers receiving 352 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

Jean-Benoit Delbrouck

AI

HI

RNMI

MB

HIM

Dave Van Veen United States

Isabella C. Wiest Germany

Benjamin Kompa United States

Asad Aali United States

Rahul Thapa United States

Christian Bluethgen Switzerland

Cara Van Uden United States

Ruilong Dan China

Joy T. Wu United States

Kaleb E Smith United States

Dave Van Veen United States

Jean-Benoit Delbrouck

155 ×0.8

AI

122 ×0.9

HI

61 ×0.6

RNMI

36 ×0.6

MB

23 ×0.9

HIM

Citations per year, relative to Jean-Benoit Delbrouck Jean-Benoit Delbrouck (= 1×) peers Dave Van Veen

Countries citing papers authored by Jean-Benoit Delbrouck

Since Specialization

Citations

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

Fields of papers citing papers by Jean-Benoit Delbrouck

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jean-Benoit Delbrouck. 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 Jean-Benoit Delbrouck. The network helps show where Jean-Benoit Delbrouck may publish in the future.

Co-authorship network of co-authors of Jean-Benoit Delbrouck

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

All Works

Sort: Min cites: Since: Top N: Style:

13 of 13 papers shown

1.

Gatidis, Sergios, et al.. (2025). Structuring Radiology Reports: Challenging LLMs with Lightweight Models. 7718–7735.

2.

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.

3.

Shah, Preya, et al.. (2025). RadGPT: A System Based on a Large Language Model That Generates Sets of Patient-Centered Materials to Explain Radiology Report Information. Journal of the American College of Radiology. 22(9). 1050–1059. 2 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.

Bluethgen, Christian, Pierre Chambon, Jean-Benoit Delbrouck, et al.. (2024). A vision–language foundation model for the generation of realistic chest X-ray images. Nature Biomedical Engineering. 9(4). 494–506. 28 indexed citations

6.

Chen, Zhihong, Maya Varma, Louis Blankemeier, et al.. (2024). GREEN: Generative Radiology Report Evaluation and Error Notation. 374–390. 8 indexed citations

7.

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 →

8.

Banerjee, Imon, Reza Arsanjani, Chadi Ayoub, et al.. (2024). Evaluating large language models in echocardiography reporting: opportunities and challenges. European Heart Journal - Digital Health. 6(3). 326–339. 4 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.

Chen, Zhihong, Maya Varma, Xiang Wan, Curtis P. Langlotz, & Jean-Benoit Delbrouck. (2023). Toward Expanding the Scope of Radiology Report Summarization to Multiple Anatomies and Modalities. 469–484. 7 indexed citations

11.

Delbrouck, Jean-Benoit, Maya Varma, Pierre Chambon, & Curtis P. Langlotz. (2023). Overview of the RadSum23 Shared Task on Multi-modal and Multi-anatomical Radiology Report Summarization. 478–482. 10 indexed citations

12.

Varma, Maya, Jean-Benoit Delbrouck, Sarah Hooper, Akshay Chaudhari, & Curtis P. Langlotz. (2023). ViLLA: Fine-Grained Vision-Language Representation Learning from Real-World Data. 22168–22178. 6 indexed citations

13.

Delbrouck, Jean-Benoit, Pierre Chambon, Christian Bluethgen, et al.. (2022). Improving the Factual Correctness of Radiology Report Generation with Semantic Rewards. 4348–4360. 30 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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