Yeganeh Madadi

479 total citations
17 papers, 249 citations indexed

About

Yeganeh Madadi is a scholar working on Radiology, Nuclear Medicine and Imaging, Health Informatics and Computer Vision and Pattern Recognition. According to data from OpenAlex, Yeganeh Madadi has authored 17 papers receiving a total of 249 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Radiology, Nuclear Medicine and Imaging, 6 papers in Health Informatics and 4 papers in Computer Vision and Pattern Recognition. Recurrent topics in Yeganeh Madadi's work include Retinal Imaging and Analysis (9 papers), Artificial Intelligence in Healthcare and Education (6 papers) and Retinal and Optic Conditions (2 papers). Yeganeh Madadi is often cited by papers focused on Retinal Imaging and Analysis (9 papers), Artificial Intelligence in Healthcare and Education (6 papers) and Retinal and Optic Conditions (2 papers). Yeganeh Madadi collaborates with scholars based in United States, Iran and Qatar. Yeganeh Madadi's co-authors include Mohammad Delsoz, Siamak Yousefi, Hina Raja, Malik Y. Kahook, Barbara Wirostko, Vahid Seydi, Shiva Mehravaran, Wuqaas M. Munir, Mohammad Soleimani and Ali R. Djalilian and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and American Journal of Ophthalmology.

In The Last Decade

Yeganeh Madadi

17 papers receiving 243 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yeganeh Madadi United States 9 141 120 73 63 31 17 249
Mohammad Delsoz United States 7 128 0.9× 117 1.0× 48 0.7× 62 1.0× 12 0.4× 15 214
Hina Raja United States 9 216 1.5× 101 0.8× 55 0.8× 145 2.3× 53 1.7× 21 295
Jocelyn Hui Lin Goh Singapore 6 198 1.4× 203 1.7× 94 1.3× 77 1.2× 27 0.9× 12 366
Samantha Min Er Yew Singapore 4 136 1.0× 200 1.7× 93 1.3× 29 0.5× 7 0.2× 8 299
David Coz United States 4 189 1.3× 82 0.7× 127 1.7× 106 1.7× 50 1.6× 5 362
Shawn Xu United States 3 175 1.2× 48 0.4× 82 1.1× 106 1.7× 37 1.2× 4 280
Saad Khan United Kingdom 5 157 1.1× 50 0.4× 37 0.5× 111 1.8× 22 0.7× 7 263
Ritwik Sinha United States 6 53 0.4× 72 0.6× 47 0.6× 11 0.2× 20 0.6× 34 279
Cristina González-Gonzalo Netherlands 6 247 1.8× 52 0.4× 57 0.8× 202 3.2× 41 1.3× 12 326
Zhouyu Guan China 6 59 0.4× 48 0.4× 55 0.8× 20 0.3× 32 1.0× 13 220

Countries citing papers authored by Yeganeh Madadi

Since Specialization
Citations

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

Fields of papers citing papers by Yeganeh Madadi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yeganeh Madadi

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

All Works

17 of 17 papers shown
1.
Jiao, Cheng, Mohammad Delsoz, Yeganeh Madadi, et al.. (2025). Diagnostic Performance of Publicly Available Large Language Models in Corneal Diseases: A Comparison with Human Specialists. Diagnostics. 15(10). 1221–1221. 5 indexed citations
2.
Delsoz, Mohammad, Yeganeh Madadi, Hina Raja, et al.. (2024). Performance of ChatGPT in Diagnosis of Corneal Eye Diseases. Cornea. 43(5). 664–670. 47 indexed citations
3.
Madadi, Yeganeh, et al.. (2024). ChatGPT Assisting Diagnosis of Neuro-Ophthalmology Diseases Based on Case Reports. Journal of Neuro-Ophthalmology. 45(3). 301–306. 14 indexed citations
4.
Raja, Hina, Mohammad Delsoz, Yeganeh Madadi, et al.. (2024). Automated Category and Trend Analysis of Scientific Articles on Ophthalmology Using Large Language Models: Development and Usability Study. JMIR Formative Research. 8. e52462–e52462. 10 indexed citations
5.
Huang, Xiaoqin, Hina Raja, Yeganeh Madadi, et al.. (2024). Predicting Glaucoma Before Onset Using a Large Language Model Chatbot. American Journal of Ophthalmology. 266. 289–299. 17 indexed citations
6.
Raja, Hina, Xiaoqin Huang, Mohammad Delsoz, et al.. (2024). Diagnosing Glaucoma Based on the Ocular Hypertension Treatment Study Dataset Using Chat Generative Pre-Trained Transformer as a Large Language Model. SHILAP Revista de lepidopterología. 5(1). 100599–100599. 6 indexed citations
7.
Madadi, Yeganeh, Hashem Abu Serhan, & Siamak Yousefi. (2024). Domain Adaptation-Based deep learning model for forecasting and diagnosis of glaucoma disease. Biomedical Signal Processing and Control. 92. 106061–106061. 12 indexed citations
8.
Madadi, Yeganeh, et al.. (2024). Applications of artificial intelligence-enabled robots and chatbots in ophthalmology: recent advances and future trends. Current Opinion in Ophthalmology. 35(3). 238–243. 10 indexed citations
9.
Delsoz, Mohammad, Hina Raja, Yeganeh Madadi, et al.. (2023). The Use of ChatGPT to Assist in Diagnosing Glaucoma Based on Clinical Case Reports. Ophthalmology and Therapy. 12(6). 3121–3132. 81 indexed citations
10.
Madadi, Yeganeh, Aboozar Monavarfeshani, Hao Chen, et al.. (2023). Artificial Intelligence Models for Cell Type and Subtype Identification Based on Single-Cell RNA Sequencing Data in Vision Science. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 20(5). 2837–2852. 3 indexed citations
11.
Madadi, Yeganeh, Jian Sun, Hao Chen, Robert W. Williams, & Siamak Yousefi. (2022). Detecting retinal neural and stromal cell classes and ganglion cell subtypes based on transcriptome data with deep transfer learning. Bioinformatics. 38(18). 4321–4329. 1 indexed citations
12.
Madadi, Yeganeh, Vahid Seydi, & Reshad Hosseini. (2021). Multi-source domain adaptation-based low-rank representation and correlation alignment. International Journal of Computers and Applications. 44(7). 670–677. 2 indexed citations
13.
Seydi, Vahid, et al.. (2021). Adversarial Image Caption Generator Network. SN Computer Science. 2(3). 4 indexed citations
14.
Rehm, Matthias, et al.. (2021). Deep transfer learning in human–robot interaction for cognitive and physical rehabilitation purposes. Pattern Analysis and Applications. 25(3). 653–677. 3 indexed citations
15.
Madadi, Yeganeh, Vahid Seydi, Kamal Nasrollahi, Reshad Hosseini, & Thomas B. Moeslund. (2020). Deep visual unsupervised domain adaptation for classification tasks: a survey. IET Image Processing. 14(14). 3283–3299. 24 indexed citations
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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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