Linda Moy

32.0k total citations · 6 hit papers
205 papers, 8.8k citations indexed

About

Linda Moy is a scholar working on Radiology, Nuclear Medicine and Imaging, Pathology and Forensic Medicine and Oncology. According to data from OpenAlex, Linda Moy has authored 205 papers receiving a total of 8.8k indexed citations (citations by other indexed papers that have themselves been cited), including 136 papers in Radiology, Nuclear Medicine and Imaging, 48 papers in Pathology and Forensic Medicine and 43 papers in Oncology. Recurrent topics in Linda Moy's work include MRI in cancer diagnosis (85 papers), Radiomics and Machine Learning in Medical Imaging (72 papers) and Advanced MRI Techniques and Applications (48 papers). Linda Moy is often cited by papers focused on MRI in cancer diagnosis (85 papers), Radiomics and Machine Learning in Medical Imaging (72 papers) and Advanced MRI Techniques and Applications (48 papers). Linda Moy collaborates with scholars based in United States, Germany and Netherlands. Linda Moy's co-authors include Ritse M. Mann, Samantha L. Heller, Nariya Cho, Mary S. Newell, Debra L. Monticciolo, Krzysztof J. Geras, Luca Bogoni, Gerardo Hermosillo Valadez, Sungheon Kim and Amy N. Melsaether and has published in prestigious journals such as Journal of Clinical Oncology, PLANT PHYSIOLOGY and Remote Sensing of Environment.

In The Last Decade

Linda Moy

196 papers receiving 8.6k citations

Hit Papers

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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.

Learning From Crowds

2010 650 citations Vikas C. Raykar, Shipeng Yu et al. Journal of Machine Learning Research profile →
Breast MRI: State of the Art

2019 534 citations Ritse M. Mann, Linda Moy et al. Radiology profile →
Breast Cancer Screening in Women at Higher-Than-Average Risk: Recommendations From the ACR

2018 439 citations Linda Moy, Bethany L. Niell et al. Journal of the American College of Radiology profile →
Breast Cancer Screening for Women at Higher-Than-Average Risk: Updated Recommendations From the ACR

2023 134 citations Linda Moy, Cindy Lee et al. Journal of the American College of Radiology profile →
Standalone AI for Breast Cancer Detection at Screening Digital Mammography and Digital Breast Tomosynthesis: A Systematic Review and Meta-Analysis

2023 85 citations Jung Hyun Yoon, Fredrik Strand et al. Radiology profile →
Environmental Sustainability and AI in Radiology: A Double-Edged Sword

2024 58 citations Florence X. Doo, Linda Moy et al. Radiology profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Linda Moy United States	49	4.8k	2.2k	1.6k	1.6k	1.6k	205	8.8k
Daniel L. Rubin United States	63	8.2k 1.7×	5.9k 2.7×	766 0.5×	1.3k 0.8×	312 0.2×	366	15.9k
Frederick Klauschen Germany	46	881 0.2×	2.1k 0.9×	1.5k 0.9×	2.6k 1.7×	571 0.4×	201	10.5k
Jarrett Rosenberg United States	45	1.7k 0.4×	407 0.2×	261 0.2×	698 0.4×	223 0.1×	182	7.3k
Marcus R. Makowski Germany	38	3.1k 0.6×	1.2k 0.5×	252 0.2×	445 0.3×	241 0.2×	362	6.7k
Justin Ko United States	22	2.6k 0.5×	3.7k 1.7×	197 0.1×	2.3k 1.4×	401 0.3×	89	9.8k
Andrew H. Beck United States	44	883 0.2×	987 0.4×	2.1k 1.3×	2.0k 1.3×	581 0.4×	123	7.3k
Peter E. Clark United States	58	677 0.1×	2.7k 1.2×	1.0k 0.6×	1.8k 1.1×	215 0.1×	420	14.4k
Mikael Lundin Finland	32	869 0.2×	869 0.4×	955 0.6×	1.4k 0.9×	263 0.2×	76	4.4k
Alexander T. Pearson United States	32	1.2k 0.3×	1.2k 0.6×	677 0.4×	1.3k 0.8×	224 0.1×	159	4.1k
Marilyn A. Roubidoux United States	35	1.8k 0.4×	1.4k 0.6×	718 0.4×	965 0.6×	652 0.4×	132	3.9k

Countries citing papers authored by Linda Moy

Since Specialization

Citations

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

Fields of papers citing papers by Linda Moy

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Linda Moy

This figure shows the co-authorship network connecting the top 25 collaborators of Linda Moy. A scholar is included among the top collaborators of Linda Moy 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 Linda Moy. Linda Moy 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:

20 of 20 papers shown

Doo, Florence X., Regent Lee, Andrea Rockall, Elizabeth Y. Rula, & Linda Moy. (2025). Medical Imaging Contrast Media Use. JAMA Network Open. 8(12). e2547304–e2547304.

Tejani, Ali S., Bardia Khosravi, Cody Savage, et al.. (2025). AI-generated Podcast Summaries of Radiology Articles: Analysis of Content and Quality. Radiology. 314(3). e243270–e243270. 1 indexed citations

Yi, Paul H., et al.. (2025). Pitfalls and Best Practices in Evaluation of AI Algorithmic Biases in Radiology. Radiology. 315(2). e241674–e241674. 2 indexed citations

Yoon, Jung Hyun, Fredrik Strand, Pascal Baltzer, et al.. (2023). Standalone AI for Breast Cancer Detection at Screening Digital Mammography and Digital Breast Tomosynthesis: A Systematic Review and Meta-Analysis. Radiology. 307(5). e222639–e222639. 85 indexed citations breakdown →

Reig, Beatriu, Eric Kim, Chloe Chhor, et al.. (2023). Problem-solving Breast MRI. Radiographics. 43(10). e230026–e230026. 2 indexed citations

Acciavatti, Raymond J., Su Hyun Lee, Beatriu Reig, et al.. (2023). Beyond Breast Density: Risk Measures for Breast Cancer in Multiple Imaging Modalities. Radiology. 306(3). e222575–e222575. 45 indexed citations

Lee, Cindy, Lenka Goldman, Lars J. Grimm, et al.. (2023). Screening mammographic performance by race and age in the National Mammography Database: 29,479,665 screening mammograms from 13,181,241 women. Breast Cancer Research and Treatment. 203(3). 599–612. 2 indexed citations

Lee, Cindy, Alana A. Lewin, Beatriu Reig, et al.. (2023). Women 75 Years Old or Older: To Screen or Not to Screen?. Radiographics. 43(5). e220166–e220166. 6 indexed citations

Tejani, Ali S., Michail E. Klontzas, Anthony A. Gatti, et al.. (2023). Updating the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) for reporting AI research. Nature Machine Intelligence. 5(9). 950–951. 9 indexed citations

10.

Mikheev, Artem, Varadan Sevilimedu, Linda Moy, et al.. (2023). Multisite MRI Intravoxel Incoherent Motion Repeatability and Reproducibility across 3 T Scanners in a Breast Diffusion Phantom: A BReast Intravoxel Incoherent Motion Multisite (BRIMM) Study. Journal of Magnetic Resonance Imaging. 59(6). 2226–2237. 8 indexed citations

11.

Witowski, Jan, Laura Heacock, Beatriu Reig, et al.. (2022). Improving breast cancer diagnostics with deep learning for MRI. Science Translational Medicine. 14(664). eabo4802–eabo4802. 79 indexed citations

12.

Zhang, Jin, Gregory Lemberskiy, Linda Moy, et al.. (2021). Measurement of cellular‐interstitial water exchange time in tumors based on diffusion‐time‐dependent diffusional kurtosis imaging. NMR in Biomedicine. 34(6). e4496–e4496. 25 indexed citations

13.

Lee, Cindy, Linda Moy, Danny R. Hughes, et al.. (2021). Radiologist Characteristics Associated with Interpretive Performance of Screening Mammography: A National Mammography Database (NMD) Study. Radiology. 300(3). 518–528. 20 indexed citations

14.

Mema, Eralda, Freya Schnabel, Jennifer Chun, et al.. (2020). The relationship of breast density in mammography and magnetic resonance imaging in women with triple negative breast cancer. European Journal of Radiology. 124. 108813–108813. 7 indexed citations

15.

Heller, Samantha L., Ana P. Lourenço, Bethany L. Niell, et al.. (2020). ACR Appropriateness Criteria® Imaging After Mastectomy and Breast Reconstruction. Journal of the American College of Radiology. 17(11). S403–S414. 19 indexed citations

16.

Price, Alison, Freya Schnabel, Jennifer Chun, et al.. (2020). Sentinel lymph node positivity in patients undergoing mastectomies for ductal carcinoma in situ (DCIS). The Breast Journal. 26(5). 931–936. 14 indexed citations

17.

Heacock, Laura, Alana A. Lewin, Abimbola Ayoola, et al.. (2019). Dynamic Contrast-Enhanced MRI Evaluation of Pathologic Complete Response in Human Epidermal Growth Factor Receptor 2 (HER2)-Positive Breast Cancer After HER2-Targeted Therapy. Academic Radiology. 27(5). e87–e93. 16 indexed citations

18.

Moy, Linda, Sungheon Kim, Steven H. Baete, et al.. (2015). Evaluation of breast cancer using intravoxel incoherent motion (IVIM) histogram analysis: comparison with malignant status, histological subtype, and molecular prognostic factors. European Radiology. 26(8). 2547–2558. 122 indexed citations

19.

Raykar, Vikas C., Shipeng Yu, Linda Zhao, et al.. (2010). Learning From Crowds. Journal of Machine Learning Research. 11(43). 1297–1322. 650 indexed citations breakdown →

20.

Yan, Yan, Rómer Rosales, Glenn Fung, et al.. (2010). Modeling annotator expertise: Learning when everybody knows a bit of something. International Conference on Artificial Intelligence and Statistics. 932–939. 113 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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