Woo Hyun Shim

2.9k total citations
103 papers, 1.9k citations indexed

About

Woo Hyun Shim is a scholar working on Radiology, Nuclear Medicine and Imaging, Neurology and Cognitive Neuroscience. According to data from OpenAlex, Woo Hyun Shim has authored 103 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 49 papers in Radiology, Nuclear Medicine and Imaging, 20 papers in Neurology and 19 papers in Cognitive Neuroscience. Recurrent topics in Woo Hyun Shim's work include Advanced MRI Techniques and Applications (20 papers), Radiomics and Machine Learning in Medical Imaging (17 papers) and Advanced Neuroimaging Techniques and Applications (14 papers). Woo Hyun Shim is often cited by papers focused on Advanced MRI Techniques and Applications (20 papers), Radiomics and Machine Learning in Medical Imaging (17 papers) and Advanced Neuroimaging Techniques and Applications (14 papers). Woo Hyun Shim collaborates with scholars based in South Korea, United States and Belarus. Woo Hyun Shim's co-authors include Ho Sung Kim, Sang Joon Kim, Ji Eun Park, Jeong Hyun Lee, Jung Hwan Baek, Young Jun Choi, Chong Hyun Suh, Jeong Hoon Kim, Tae Yong Kim and Young Kee Shong and has published in prestigious journals such as PLoS ONE, Neurology and Stroke.

In The Last Decade

Woo Hyun Shim

97 papers receiving 1.9k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Woo Hyun Shim South Korea	23	1.0k	252	248	231	182	103	1.9k
Joeky T. Senders Netherlands	18	385 0.4×	99 0.4×	272 1.1×	325 1.4×	253 1.4×	36	1.6k
Yasutaka Fushimi Japan	29	1.4k 1.4×	90 0.4×	232 0.9×	245 1.1×	801 4.4×	170	3.1k
Se Jin Cho South Korea	22	654 0.7×	531 2.1×	207 0.8×	108 0.5×	240 1.3×	54	1.7k
Jae Hyoung Kim South Korea	31	901 0.9×	144 0.6×	672 2.7×	257 1.1×	897 4.9×	200	3.4k
Ángel Alberich‐Bayarri Spain	28	745 0.7×	37 0.1×	284 1.1×	145 0.6×	108 0.6×	104	1.6k
Akifumi Hagiwara Japan	28	1.9k 1.9×	49 0.2×	124 0.5×	223 1.0×	371 2.0×	151	2.7k
Floris H. P. van Velden Netherlands	26	1.8k 1.8×	79 0.3×	86 0.3×	73 0.3×	64 0.4×	95	2.4k
Byung Se Choi South Korea	25	498 0.5×	57 0.2×	475 1.9×	221 1.0×	697 3.8×	101	2.0k
Bihong T. Chen United States	18	475 0.5×	108 0.4×	45 0.2×	198 0.9×	102 0.6×	91	1.2k
Arnaud J.P.E. Vincent Netherlands	24	905 0.9×	184 0.7×	765 3.1×	1.1k 4.9×	764 4.2×	99	3.4k

Countries citing papers authored by Woo Hyun Shim

Since Specialization

Citations

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

Fields of papers citing papers by Woo Hyun Shim

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Woo Hyun Shim. 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 Woo Hyun Shim. The network helps show where Woo Hyun Shim may publish in the future.

Co-authorship network of co-authors of Woo Hyun Shim

This figure shows the co-authorship network connecting the top 25 collaborators of Woo Hyun Shim. A scholar is included among the top collaborators of Woo Hyun Shim 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 Woo Hyun Shim. Woo Hyun Shim 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

Heo, Hwon, et al.. (2025). Adherence of Studies on Large Language Models for Medical Applications Published in Leading Medical Journals According to the MI-CLEAR-LLM Checklist. Korean Journal of Radiology. 26(4). 304–304. 5 indexed citations

Jeong, So Yeong, Chong Hyun Suh, Jae‐Sung Lim, et al.. (2025). Incidence of Amyloid-Related Imaging Abnormalities in Phase III Clinical Trials of Anti-Amyloid-β Immunotherapy. Neurology. 104(8). e213483–e213483. 6 indexed citations

Park, Jun Sung, Jisun Hwang, Pyeong Hwa Kim, et al.. (2025). Accuracy of Large Language Models in Detecting Cases Requiring Immediate Reporting in Pediatric Radiology: A Feasibility Study Using Publicly Available Clinical Vignettes. Korean Journal of Radiology. 26(9). 855–855.

Jo, Sungyang, Ji‐Hyun Lee, Hwon Heo, et al.. (2025). Connectivity-Based Analysis of the Stimulation Effects of Globus Pallidus Interna Deep Brain Stimulation in Parkinson’s Disease: A Focus on Freezing of Gait. Journal of Movement Disorders. 18(4). 327–336.

Suh, Chong Hyun, Kyu Sung Choi, Jinyoung Kim, et al.. (2024). Automated Idiopathic Normal Pressure Hydrocephalus Diagnosis via Artificial Intelligence–Based 3D T1 MRI Volumetric Analysis. American Journal of Neuroradiology. 46(1). 33–40. 1 indexed citations

Heo, Hwon, et al.. (2024). Cortical volumetric changes after cochlear implantation in postlingually deaf adults: correlation with speech perception abilities. Scientific Reports. 14(1). 17524–17524.

Shim, Woo Hyun, Chong Hyun Suh, Hwon Heo, et al.. (2024). Comparing Large Language Model and Human Reader Accuracy with New England Journal of Medicine Image Challenge Case Image Inputs. Radiology. 313(3). e241668–e241668. 9 indexed citations

Shim, Woo Hyun, Chong Hyun Suh, Hwon Heo, et al.. (2024). Comparing Diagnostic Accuracy of Radiologists versus GPT-4V and Gemini Pro Vision Using Image Inputs from Diagnosis Please Cases. Radiology. 312(1). e240273–e240273. 35 indexed citations

Heo, Hwon, Min‐Seok Kim, Seung‐Eun Lee, et al.. (2023). Amphiregulin normalizes altered circuit connectivity for social dominance of the CRTC3 knockout mouse. Molecular Psychiatry. 28(11). 4655–4665. 5 indexed citations

10.

Kim, Pyeong Hwa, Hee Mang Yoon, Jeong Rye Kim, et al.. (2023). Bone Age Assessment Using Artificial Intelligence in Korean Pediatric Population: A Comparison of Deep-Learning Models Trained With Healthy Chronological and Greulich-Pyle Ages as Labels. Korean Journal of Radiology. 24(11). 1151–1151. 17 indexed citations

11.

Heo, Hwon, Chong Hyun Suh, Woo Hyun Shim, et al.. (2023). Improved diagnostic performance of susceptibility-weighted imaging with compressed sensing-sensitivity encoding and neuromelanin-sensitive MRI for Parkinson's disease and atypical Parkinsonism. Clinical Radiology. 79(1). e102–e111. 2 indexed citations

12.

Park, Ho Young, Chong Hyun Suh, Woo Hyun Shim, et al.. (2022). Diagnostic yield of TOF-MRA for detecting incidental vascular lesions in patients with cognitive impairment: An observational cohort study. Frontiers in Neurology. 13. 958037–958037. 1 indexed citations

13.

Paik, Wooyul, Young‐Chul Choi, Young‐Min Lim, et al.. (2021). Clinical Features and Brain MRI Findings in Korean Patients with AGel Amyloidosis. Yonsei Medical Journal. 62(5). 431–431. 3 indexed citations

14.

Park, Seong Jong, Hwa Jin Cho, Oyeon Kwon, et al.. (2021). Development and validation of a deep-learning-based pediatric early warning system: A single-center study. Biomedical Journal. 45(1). 155–168. 17 indexed citations

15.

Yoo, Soyoung, et al.. (2020). De-Identification of Facial Features in Magnetic Resonance Images: Software Development Using Deep Learning Technology. Journal of Medical Internet Research. 22(12). e22739–e22739. 22 indexed citations

16.

Park, Ji Eun, Jessica Yun, Sang Joon Kim, et al.. (2019). Intra-individual correlations between quantitative THK-5351 PET and MRI-derived cortical volume in Alzheimer’s disease differ according to disease severity and amyloid positivity. PLoS ONE. 14(12). e0226265–e0226265. 5 indexed citations

17.

Suh, Chong Hyun, Young Jun Choi, Jong Jin Lee, et al.. (2017). Comparison of Core-Needle Biopsy and Fine-Needle Aspiration for Evaluating Thyroid Incidentalomas Detected by ¹⁸ F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography: A Propensity Score Analysis. Thyroid. 27(10). 1258–1266. 4 indexed citations

18.

Park, Ji Eun, Ho Sung Kim, Ho Sung Kim, et al.. (2017). Perfusion of surgical cavity wall enhancement in early post-treatment MR imaging may stratify the time-to-progression in glioblastoma. PLoS ONE. 12(7). e0181933–e0181933. 4 indexed citations

19.

Park, Ji Eun, Bumwoo Park, Sang Joon Kim, et al.. (2017). Improved Diagnostic Accuracy of Alzheimer's Disease by Combining Regional Cortical Thickness and Default Mode Network Functional Connectivity: Validated in the Alzheimer's Disease Neuroimaging Initiative Set. Korean Journal of Radiology. 18(6). 983–983. 11 indexed citations

20.

Choi, Young Jun, Jung Hwan Baek, Seung Hee Baek, et al.. (2015). Web-Based Malignancy Risk Estimation for Thyroid Nodules Using Ultrasonography Characteristics: Development and Validation of a Predictive Model. Thyroid. 25(12). 1306–1312. 33 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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