Peter Chang

3.4k total citations
71 papers, 2.5k citations indexed

About

Peter Chang is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Peter Chang has authored 71 papers receiving a total of 2.5k indexed citations (citations by other indexed papers that have themselves been cited), including 46 papers in Radiology, Nuclear Medicine and Imaging, 22 papers in Artificial Intelligence and 19 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Peter Chang's work include Radiomics and Machine Learning in Medical Imaging (32 papers), AI in cancer detection (22 papers) and MRI in cancer diagnosis (7 papers). Peter Chang is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (32 papers), AI in cancer detection (22 papers) and MRI in cancer diagnosis (7 papers). Peter Chang collaborates with scholars based in United States, South Korea and China. Peter Chang's co-authors include Steven M. Greenberg, Daniel Chow, Simukayi Mutasa, S C Silverstein, Sachin Jambawalikar, Qing Zhang, Gary Bokoch, Pabbathi G. Reddy, Dianne Cox and Michael Z. Liu and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Biological Chemistry and The Journal of Experimental Medicine.

In The Last Decade

Peter Chang

66 papers receiving 2.5k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Peter Chang United States 26 1.3k 662 400 395 355 71 2.5k
Gabriele Campanella United States 21 1.0k 0.8× 1.2k 1.8× 856 2.1× 654 1.7× 818 2.3× 31 3.9k
Peter Bult Netherlands 36 1.6k 1.2× 1.2k 1.8× 616 1.5× 440 1.1× 79 0.2× 120 5.3k
Håvard E. Danielsen Norway 32 613 0.5× 510 0.8× 839 2.1× 949 2.4× 132 0.4× 104 3.5k
Niels Halama Germany 32 1.2k 0.9× 977 1.5× 1.1k 2.8× 588 1.5× 2.1k 5.9× 114 5.7k
Niels Grabe Germany 33 606 0.5× 419 0.6× 1.2k 3.1× 626 1.6× 907 2.6× 106 4.6k
Arvind Rao United States 32 1.5k 1.1× 503 0.8× 884 2.2× 761 1.9× 625 1.8× 131 4.0k
Lucian Beer Austria 23 767 0.6× 293 0.4× 357 0.9× 333 0.8× 127 0.4× 82 1.9k
Alexander Sauter Germany 27 1.5k 1.1× 161 0.2× 404 1.0× 487 1.2× 74 0.2× 115 2.8k
James C. Folk United States 42 4.0k 3.1× 284 0.4× 711 1.8× 218 0.6× 84 0.2× 174 7.1k
D.M. Jukic United States 26 328 0.2× 784 1.2× 694 1.7× 221 0.6× 367 1.0× 107 2.7k

Countries citing papers authored by Peter Chang

Since Specialization
Citations

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

Fields of papers citing papers by Peter Chang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Peter Chang

This figure shows the co-authorship network connecting the top 25 collaborators of Peter Chang. A scholar is included among the top collaborators of Peter Chang 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 Peter Chang. Peter Chang 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.
Ravi, Praful, Lucia Kwak, Andrés Acosta, et al.. (2025). Long-term Outcomes and Prognostic Impact of Residual Cancer Burden After Intensified Neoadjuvant Therapy in High-risk Prostate Cancer. European Urology. 87(6). 643–650. 1 indexed citations
2.
Chang, Peter, et al.. (2025). Label-Free Prediction of Fluorescently Labeled Fibrin Networks. Biomaterials Research. 29. 211–211.
3.
Houshmand, Sina, Shahriar Faghani, Peter Chang, et al.. (2025). Cross-Institutional Evaluation of Large Language Models for Radiology Diagnosis Extraction: A Prompt-Engineering Perspective. Journal of Imaging Informatics in Medicine. 39(1). 989–994.
4.
Weinberg, Brent D., Peter Chang, Daniel Chow, et al.. (2024). Enhancing Radiologist Efficiency with AI: A Multi-Reader Multi-Case Study on Aortic Dissection Detection and Prioritization. Diagnostics. 14(23). 2689–2689. 2 indexed citations
5.
Chang, Peter, et al.. (2024). Diagnostic Performance of a Deep Learning-Powered Application for Aortic Dissection Triage Prioritization and Classification. Diagnostics. 14(17). 1877–1877. 5 indexed citations
6.
Li, Charles, et al.. (2024). Artificial Intelligence Efficacy as a Function of Trainee Interpreter Proficiency: Lessons from a Randomized Controlled Trial. American Journal of Neuroradiology. 45(11). 1647–1654. 1 indexed citations
7.
Davis, Adam, et al.. (2024). Deep Learning–Based ASPECTS Algorithm Enhances Reader Performance and Reduces Interpretation Time. American Journal of Neuroradiology. 46(3). 544–551. 1 indexed citations
8.
Khosravi, Bardia, Elham Mahmoudi, Pouria Rouzrokh, et al.. (2024). A Guideline for Open-Source Tools to Make Medical Imaging Data Ready for Artificial Intelligence Applications: A Society of Imaging Informatics in Medicine (SIIM) Survey. Journal of Imaging Informatics in Medicine. 37(5). 2015–2024.
9.
Chang, Peter, et al.. (2024). Performance and clinical utility of an artificial intelligence-enabled tool for pulmonary embolism detection. Clinical Imaging. 113. 110245–110245. 11 indexed citations
10.
Greniér, Philippe, et al.. (2023). Deep Learning-Based Algorithm for Automatic Detection of Pulmonary Embolism in Chest CT Angiograms. Diagnostics. 13(7). 1324–1324. 23 indexed citations
11.
Hill, Virginia, Andreas M. Rauschecker, Yvonne W. Lui, et al.. (2023). Critical Appraisal of Artificial Intelligence–Enabled Imaging Tools Using the Levels of Evidence System. American Journal of Neuroradiology. 44(5). E21–E28. 18 indexed citations
12.
Shahraki, Kourosh, Stephen C. Hunter, So Young Kim, et al.. (2023). Deep Learning Approach for Differentiating Etiologies of Pediatric Retinal Hemorrhages: A Multicenter Study. International Journal of Molecular Sciences. 24(20). 15105–15105. 5 indexed citations
13.
Kuoy, Edward, Justin Glavis‐Bloom, Jennifer E. Soun, et al.. (2022). Point-of-Care Brain MRI: Preliminary Results from a Single-Center Retrospective Study. Radiology. 305(3). 666–671. 20 indexed citations
14.
Mongan, John, Marc Kohli, Roozbeh Houshyar, et al.. (2022). Automated detection of IVC filters on radiographs with deep convolutional neural networks. Abdominal Radiology. 48(2). 758–764. 2 indexed citations
15.
Houshyar, Roozbeh, Justin Glavis‐Bloom, Thanh‐Lan Bui, et al.. (2021). Outcomes of Artificial Intelligence Volumetric Assessment of Kidneys and Renal Tumors for Preoperative Assessment of Nephron-Sparing Interventions. Journal of Endourology. 35(9). 1411–1418. 11 indexed citations
16.
Chow, Daniel, et al.. (2020). Updates on Deep Learning and Glioma. Neuroimaging Clinics of North America. 30(4). 493–503. 14 indexed citations
17.
Chang, Peter, et al.. (2019). Deep Learning for Detection of Complete Anterior Cruciate Ligament Tear. Journal of Digital Imaging. 32(6). 980–986. 105 indexed citations
18.
Ha, Richard, Peter Chang, Yu Gan, et al.. (2019). Fully Automated Postlumpectomy Breast Margin Assessment Utilizing Convolutional Neural Network Based Optical Coherence Tomography Image Classification Method. Academic Radiology. 27(5). e81–e86. 31 indexed citations
19.
Ha, Richard, Peter Chang, Jenika Karcich, et al.. (2018). Predicting Post Neoadjuvant Axillary Response Using a Novel Convolutional Neural Network Algorithm. Annals of Surgical Oncology. 25(10). 3037–3043. 26 indexed citations
20.
Ha, Richard, Peter Chang, Jenika Karcich, et al.. (2018). Axillary Lymph Node Evaluation Utilizing Convolutional Neural Networks Using MRI Dataset. Journal of Digital Imaging. 31(6). 851–856. 52 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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