Jean Feng

1.4k total citations · 2 hit papers
41 papers, 576 citations indexed

About

Jean Feng is a scholar working on Surgery, Artificial Intelligence and Oncology. According to data from OpenAlex, Jean Feng has authored 41 papers receiving a total of 576 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Surgery, 10 papers in Artificial Intelligence and 7 papers in Oncology. Recurrent topics in Jean Feng's work include Machine Learning in Healthcare (6 papers), Gestational Diabetes Research and Management (5 papers) and Sepsis Diagnosis and Treatment (4 papers). Jean Feng is often cited by papers focused on Machine Learning in Healthcare (6 papers), Gestational Diabetes Research and Management (5 papers) and Sepsis Diagnosis and Treatment (4 papers). Jean Feng collaborates with scholars based in United States, China and France. Jean Feng's co-authors include Romain Pirracchio, Alan Hubbard, Andrew Bishara, Rachael V. Phillips, Leo Anthony Celi, Ivana Malenica, Charles P. Quesenberry, Noah Simon, Stephen Sidney and Erica P. Gunderson and has published in prestigious journals such as JAMA, Journal of Clinical Oncology and SHILAP Revista de lepidopterología.

In The Last Decade

Jean Feng

36 papers receiving 562 citations

Hit Papers

Clinical artificial intelligence quality improvement: tow... 2022 2026 2023 2024 2022 2025 50 100 150
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.

  1. Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare
    2022 195 citations Jean Feng, Rachael V. Phillips et al. npj Digital Medicine profile →
  2. Optimal Vasopressin Initiation in Septic Shock
    2025 15 citations Jean Feng, Romain Pirracchio et al. profile →

Peers

Jean Feng
Daniel X. Yang United States
Lin Guo China
Jeevesh Kapur Singapore
Byron H. Smith United States
Munetoshi Akazawa Japan
Charalampos Chrelias Greece
Yimin Qu China
Joseph B. Leader United States
Kensuke Matsushita France
Jennifer Braun United States
Daniel X. Yang United States
Jean Feng
Citations per year, relative to Jean Feng Jean Feng (= 1×) peers Daniel X. Yang

Countries citing papers authored by Jean Feng

Since Specialization
Citations

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

Fields of papers citing papers by Jean Feng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jean Feng

This figure shows the co-authorship network connecting the top 25 collaborators of Jean Feng. A scholar is included among the top collaborators of Jean Feng 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 Feng. Jean Feng 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.
Feng, Jean, et al.. (2025). Not All Clinical AI Monitoring Systems Are Created Equal: Review and Recommendations. NEJM AI. 2(2). 6 indexed citations
2.
Kouzy, Ramez, Michael K. Rooney, Jean Feng, et al.. (2025). The state of the art in artificial intelligence and digital pathology in prostate cancer. Nature Reviews Urology. 23(1). 13–28. 1 indexed citations
3.
Kolla, Likhitha, Kristin A. Linn, Amol S. Navathe, et al.. (2025). Performance Drift in a Nationally Deployed Population Health Risk Algorithm in the US Veterans Health Administration. JAMA Health Forum. 6(8). e252717–e252717. 1 indexed citations
4.
Feng, Jean, et al.. (2025). Mask-Q attention network for flare removal. Neurocomputing. 637. 130100–130100.
5.
Chang, Chao‐Yuan, Jean Feng, Lauren Boreta, et al.. (2025). Patterns in Symptoms Preceding Acute Care in Patients With Cancer. JAMA Network Open. 8(4). e256366–e256366.
6.
Tseng, Zian H., James W. Salazar, Julianne Wojciak, et al.. (2024). Heritable Burden of Community Sudden Death by Autopsy and Molecular Phenotyping for Precision Genotype Correlation. JACC. Clinical electrophysiology. 11(3). 471–481.
7.
Wang, Jane, Francesca Tozzi, Amir Ashraf‐Ganjouei, et al.. (2024). Machine learning improves prediction of postoperative outcomes after gastrointestinal surgery: a systematic review and meta-analysis. Journal of Gastrointestinal Surgery. 28(6). 956–965. 7 indexed citations
8.
Caudal, Arianne, Yu Liu, Paul Pang, et al.. (2024). Transcriptomic Profiling of Human Myocardium at Sudden Death to Define Vulnerable Substrate for Lethal Arrhythmias. JACC. Clinical electrophysiology. 11(1). 143–155. 1 indexed citations
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11.
Feng, Jean, Kenzo Hirose, Eric K. Nakakura, et al.. (2023). Hypertension Requiring Medication Use: a Silent Predictor of Poor Outcomes After Pancreaticoduodenectomy. Journal of Gastrointestinal Surgery. 27(2). 328–336. 3 indexed citations
12.
Fong, Nicholas, et al.. (2023). IntraCranial pressure prediction AlgoRithm using machinE learning (I-CARE): Training and Validation Study. Critical Care Explorations. 6(1). e1024–e1024. 4 indexed citations
13.
14.
Marano, Paul, Yifei Ma, Rachel J. Stern, et al.. (2023). Predictors of All-Cause 30-Day Readmissions in Patients with Heart Failure at an Urban Safety Net Hospital: The Importance of Social Determinants of Health and Mental Health. SHILAP Revista de lepidopterología. 10. 100060–100060. 1 indexed citations
15.
Feng, Jean, Rachael V. Phillips, Ivana Malenica, et al.. (2022). Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare. npj Digital Medicine. 5(1). 66–66. 195 indexed citations breakdown →
16.
Feng, Jean, D A Shaw, Vladimir N. Minin, Noah Simon, & F. A. Matsen. (2019). Survival analysis of DNA mutation motifs with penalized proportional hazards. The Annals of Applied Statistics. 13(2). 1268–1294. 10 indexed citations
17.
Feng, Jean, Brian D. Williamson, Noah Simon, & Marco Carone. (2018). Nonparametric variable importance using an augmented neural network with multi-task learning. International Conference on Machine Learning. 1496–1505. 2 indexed citations
18.
Ehrlich, Samantha F., Monique M. Hedderson, Susan D. Brown, et al.. (2017). Moderate intensity sports and exercise is associated with glycaemic control in women with gestational diabetes. Diabetes & Metabolism. 43(5). 416–423. 12 indexed citations
19.
Gunderson, Erica P., Charles P. Quesenberry, David R. Jacobs, et al.. (2010). Longitudinal Study of Prepregnancy Cardiometabolic Risk Factors and Subsequent Risk of Gestational Diabetes Mellitus: The CARDIA Study. American Journal of Epidemiology. 172(10). 1131–1143. 75 indexed citations
20.
Wu, Qingkai, et al.. (2004). Gestational diabetes mellitus in Chinese women. International Journal of Gynecology & Obstetrics. 88(2). 122–126. 4 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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