John T. Moon

549 total citations
26 papers, 335 citations indexed

About

John T. Moon is a scholar working on Surgery, Pulmonary and Respiratory Medicine and Health Informatics. According to data from OpenAlex, John T. Moon has authored 26 papers receiving a total of 335 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Surgery, 6 papers in Pulmonary and Respiratory Medicine and 5 papers in Health Informatics. Recurrent topics in John T. Moon's work include Artificial Intelligence in Healthcare and Education (5 papers), Radiomics and Machine Learning in Medical Imaging (4 papers) and Gastroesophageal reflux and treatments (3 papers). John T. Moon is often cited by papers focused on Artificial Intelligence in Healthcare and Education (5 papers), Radiomics and Machine Learning in Medical Imaging (4 papers) and Gastroesophageal reflux and treatments (3 papers). John T. Moon collaborates with scholars based in United States and Canada. John T. Moon's co-authors include Hanzhou Li, Judy Wawira Gichoya, Hari Trivedi, Saptarshi Purkayastha, Leo Anthony Celi, Massimo Arcerito, Patricia Balthazar, Óscar Bernal, Deepak Iyer and Imon Banerjee and has published in prestigious journals such as Biosensors and Bioelectronics, Journal of Neurotrauma and BMJ Open.

In The Last Decade

John T. Moon

18 papers receiving 332 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
John T. Moon United States 7 178 101 83 67 35 26 335
Eyal Shachar Israel 8 112 0.6× 70 0.7× 72 0.9× 54 0.8× 17 0.5× 18 248
Ameer Khan United Kingdom 6 71 0.4× 111 1.1× 51 0.6× 43 0.6× 17 0.5× 14 260
Y Zhang China 7 99 0.6× 70 0.7× 36 0.4× 30 0.4× 14 0.4× 33 258
Anna Seehofnerová United States 8 109 0.6× 139 1.4× 129 1.6× 32 0.5× 48 1.4× 12 412
Oğuz Kuşçu Türkiye 12 60 0.3× 32 0.3× 22 0.3× 75 1.1× 85 2.4× 37 299
Nicholas T. Befera United States 11 43 0.2× 163 1.6× 38 0.5× 55 0.8× 45 1.3× 30 312
Iain B. McInnes United Kingdom 2 88 0.5× 48 0.5× 94 1.1× 23 0.3× 23 0.7× 3 302
José Florencio F. Lapeña Philippines 8 66 0.4× 23 0.2× 30 0.4× 20 0.3× 36 1.0× 40 174
Janardhana Ponnatapura United States 7 146 0.8× 142 1.4× 102 1.2× 77 1.1× 26 0.7× 18 359
Leonard E. Estephan United States 8 76 0.4× 22 0.2× 28 0.3× 33 0.5× 53 1.5× 21 241

Countries citing papers authored by John T. Moon

Since Specialization
Citations

This map shows the geographic impact of John T. Moon'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 John T. Moon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John T. Moon more than expected).

Fields of papers citing papers by John T. Moon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John T. Moon. 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 John T. Moon. The network helps show where John T. Moon may publish in the future.

Co-authorship network of co-authors of John T. Moon

This figure shows the co-authorship network connecting the top 25 collaborators of John T. Moon. A scholar is included among the top collaborators of John T. Moon 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 John T. Moon. John T. Moon 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.
Oh, Saewoong, Robert Herbert, Jimin Lee, et al.. (2025). Wireless, battery-free self-detecting smart arteriovenous graft for stenosis diagnosis in dialysis patients. Biosensors and Bioelectronics. 293. 118088–118088.
2.
Li, Hanzhou, John T. Moon, Zachary Bercu, et al.. (2025). Evaluation of Multilingual Simplifications of IR Procedural Reports Using GPT-4. Journal of Vascular and Interventional Radiology. 36(4). 696–703.e1.
3.
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5.
Moon, John T., et al.. (2025). Cryoneurolysis as a Neuroregenerative Intervention for Chronic Painful Mononeuropathies: A Four-Patient Case Series and Discussion. CardioVascular and Interventional Radiology. 48(10). 1499–1504.
6.
Li, Hanzhou, et al.. (2024). Enhancing radiology training with GPT-4: Pilot analysis of automated feedback in trainee preliminary reports. Current Problems in Diagnostic Radiology. 54(2). 151–158. 5 indexed citations
7.
Moon, John T., Jenny Nguyen, Deepak Iyer, et al.. (2024). Analysis of percutaneous nephrostomy exchange intervals: insights from a retrospective Merative MarketScan analysis between 2009-2021. World Journal of Urology. 42(1). 424–424. 1 indexed citations
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Li, Hanzhou, John T. Moon, Deepak Iyer, et al.. (2023). Decoding radiology reports: Potential application of OpenAI ChatGPT to enhance patient understanding of diagnostic reports. Clinical Imaging. 101. 137–141. 67 indexed citations
10.
Li, Hanzhou, John T. Moon, Saptarshi Purkayastha, et al.. (2023). Ethics of large language models in medicine and medical research. The Lancet Digital Health. 5(6). e333–e335. 166 indexed citations
11.
Moon, John T., Menelaos Konstantinidis, Nariman Nezami, et al.. (2023). Characteristics of pivotal clinical trials of FDA-approved endovascular devices between 2000 and 2018: An interrupted time series analysis. Journal of Clinical and Translational Science. 7(1). e67–e67. 1 indexed citations
12.
Lee, Tai-Lin, et al.. (2023). Understanding Radiological Journal Views and Policies on Large Language Models in Academic Writing. Journal of the American College of Radiology. 21(4). 678–682. 5 indexed citations
13.
Arcerito, Massimo, et al.. (2022). Esophageal Achalasia: From Laparoscopic to Robotic Heller Myotomy and Dor Fundoplication. JSLS Journal of the Society of Laparoscopic & Robotic Surgeons. 26(3). e2022.00027–e2022.00027. 11 indexed citations
14.
Chockalingam, Arun, Menelaos Konstantinidis, John T. Moon, et al.. (2022). Surgical resection, radiotherapy and percutaneous thermal ablation for treatment of stage 1 non-small cell lung cancer: protocol for a systematic review and network meta-analysis. BMJ Open. 12(6). e057638–e057638. 5 indexed citations
15.
Goodwin, Justin, Leo L. Tsai, John T. Moon, et al.. (2021). In vivo detection of distal tumor glycolytic flux stimulated by hepatic ablation in a breast cancer model using hyperpolarized 13C MRI. Magnetic Resonance Imaging. 80. 90–97. 1 indexed citations
16.
Arcerito, Massimo, et al.. (2020). Robotic Fundoplication for Large Paraesophageal Hiatal Hernias. JSLS Journal of the Society of Laparoscopic & Robotic Surgeons. 24(1). e2019.00054–e2019.00054. 10 indexed citations
17.
Arcerito, Massimo, et al.. (2018). Robotic Fundoplication for Gastroesophageal Reflux Disease and Hiatal Hernia: Initial Experience and Outcome. The American Surgeon. 84(12). 1945–1950. 7 indexed citations
18.
Arcerito, Massimo, et al.. (2016). Robotic Inguinal Hernia Repair: Technique and Early Experience. The American Surgeon. 82(10). 1014–1017. 31 indexed citations
19.
Moon, John T., et al.. (2016). Expression of glial CBP in steroid mediated neuroprotection in male and female zebra finches. Journal of Chemical Neuroanatomy. 79. 32–37. 4 indexed citations
20.
Duncan, Kelli A., et al.. (2013). Injury-Induced Expression of Glial Androgen Receptor in the Zebra Finch Brain. Journal of Neurotrauma. 30(22). 1919–1924. 16 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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