Jarrel Seah

1.2k total citations
28 papers, 548 citations indexed

About

Jarrel Seah is a scholar working on Radiology, Nuclear Medicine and Imaging, Health Informatics and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Jarrel Seah has authored 28 papers receiving a total of 548 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Radiology, Nuclear Medicine and Imaging, 6 papers in Health Informatics and 5 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Jarrel Seah's work include Radiomics and Machine Learning in Medical Imaging (10 papers), Radiology practices and education (10 papers) and COVID-19 diagnosis using AI (8 papers). Jarrel Seah is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (10 papers), Radiology practices and education (10 papers) and COVID-19 diagnosis using AI (8 papers). Jarrel Seah collaborates with scholars based in Australia, United States and Switzerland. Jarrel Seah's co-authors include Catherine M Jones, Nazanin Esmaili, Quinlan D. Buchlak, Frank Gaillard, Andrew Dixon, Luke Oakden‐Rayner, Cyril Tang, Meng Law, Ben Hachey and Peter Brotchie and has published in prestigious journals such as SHILAP Revista de lepidopterología, Annals of Surgery and Scientific Reports.

In The Last Decade

Jarrel Seah

25 papers receiving 537 citations

Peers

Jarrel Seah

RNMI

PRM

Myeongchan Kim South Korea

Mohammad Mansouri United States

Kicky G. van Leeuwen Netherlands

Swetha Tanamala United States

Mark Cicero Canada

Sasank Chilamkurthy United States

Thomas Weikert Switzerland

Vasantha Kumar Venugopal India

Takahiro Nakao Japan

Andrew Makmur Singapore

Myeongchan Kim South Korea

Jarrel Seah

299 ×0.9

RNMI

127 ×0.8

116 ×0.9

PRM

176 ×1.5

97 ×1.2

Citations per year, relative to Jarrel Seah Jarrel Seah (= 1×) peers Myeongchan Kim

Countries citing papers authored by Jarrel Seah

Since Specialization

Citations

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

Fields of papers citing papers by Jarrel Seah

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jarrel Seah

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

All Works

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20 of 20 papers shown

Ditchfield, Michael, et al.. (2025). Enhancing Radiographic Diagnosis: CycleGAN-Based Methods for Reducing Cast Shadow Artifacts in Wrist Radiographs. Journal of Imaging Informatics in Medicine. 38(5). 2729–2738.

Seah, Jarrel, et al.. (2025). Drafting the Future: The Dawn of AI Report Generation in Radiology. Radiology. 316(1). e243378–e243378.

Milne, Michael, Quinlan D. Buchlak, Nazanin Esmaili, et al.. (2024). Applications and potential of machine, learning augmented chest X-ray interpretation in cardiology. Minerva Cardiology and Angiology. 73(1). 8–22.

Milne, Michael, Quinlan D. Buchlak, Jason Chiang, et al.. (2023). Machine Learning Augmented Interpretation of Chest X-rays: A Systematic Review. Diagnostics. 13(4). 743–743. 20 indexed citations

Tang, Cyril, Jarrel Seah, Michael Milne, et al.. (2023). Analysis of Line and Tube Detection Performance of a Chest X-ray Deep Learning Model to Evaluate Hidden Stratification. Diagnostics. 13(14). 2317–2317. 4 indexed citations

Sinclair, Benjamin, Jarrel Seah, Lucy Vivash, et al.. (2022). Machine learning approaches for imaging‐based prognostication of the outcome of surgery for mesial temporal lobe epilepsy. Epilepsia. 63(5). 1081–1092. 20 indexed citations

Seah, Jarrel, et al.. (2022). Retrospective analysis and prospective validation of an AI-based software for intracranial haemorrhage detection at a high-volume trauma centre. Scientific Reports. 12(1). 19885–19885. 16 indexed citations

Hillis, James, Bernardo C. Bizzo, Sarah Mercaldo, et al.. (2022). Evaluation of an Artificial Intelligence Model for Detection of Pneumothorax and Tension Pneumothorax in Chest Radiographs. JAMA Network Open. 5(12). e2247172–e2247172. 29 indexed citations

Buchlak, Quinlan D., Michael Milne, Jarrel Seah, et al.. (2022). Charting the potential of brain computed tomography deep learning systems. Journal of Clinical Neuroscience. 99. 217–223. 19 indexed citations

10.

Seah, Jarrel, Tom Boeken, Marc Sapoval, & Gerard S. Goh. (2022). Prime Time for Artificial Intelligence in Interventional Radiology. CardioVascular and Interventional Radiology. 45(3). 283–289. 21 indexed citations

11.

Seah, Jarrel, Jay Gajera, Helen Kavnoudias, et al.. (2021). CLiP, catheter and line position dataset. Scientific Data. 8(1). 285–285. 10 indexed citations

12.

Jones, Catherine M, Michael Milne, Cyril Tang, et al.. (2021). Assessment of the effect of a comprehensive chest radiograph deep learning model on radiologist reports and patient outcomes: a real-world observational study. BMJ Open. 11(12). e052902–e052902. 28 indexed citations

13.

Seah, Jarrel, Cyril Tang, Quinlan D. Buchlak, et al.. (2021). Effect of a comprehensive deep-learning model on the accuracy of chest x-ray interpretation by radiologists: a retrospective, multireader multicase study. The Lancet Digital Health. 3(8). e496–e506. 128 indexed citations

14.

Law, Meng, Jarrel Seah, & George Shih. (2021). Artificial intelligence and medical imaging: applications, challenges and solutions. The Medical Journal of Australia. 214(10). 450–450. 7 indexed citations

15.

Mitra, Biswadev, et al.. (2021). Delayed intracranial hemorrhage after trauma. Brain Injury. 35(4). 484–489. 2 indexed citations

16.

Clements, Warren, et al.. (2021). Left Common Iliac Vein Compression in Patients with May-Thurner Syndrome. SHILAP Revista de lepidopterología. 8(Suppl 1). S41–S45. 2 indexed citations

17.

Pawar, Kamlesh, Zhaolin Chen, Jarrel Seah, et al.. (2020). Clinical utility of deep learning motion correction for T1 weighted MPRAGE MR images. European Journal of Radiology. 133. 109384–109384. 13 indexed citations

18.

Seah, Jarrel, et al.. (2019). Missed opportunities for HIV testing persist despite a single educational intervention: how can we close this evidence‐practice gap?. Internal Medicine Journal. 50(3). 285–292. 6 indexed citations

19.

Schneider, Hans G., William Chan, Jarrel Seah, et al.. (2018). Rapid and safe discharge from the emergency department: A single troponin to exclude acute myocardial infarction. Emergency Medicine Australasia. 30(4). 486–493. 4 indexed citations

20.

Seah, Jarrel, et al.. (2017). Detection of prostate cancer on multiparametric MRI. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 10134. 1013429–1013429. 22 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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