Eyal Klang

11.2k total citations · 3 hit papers
277 papers, 6.3k citations indexed

About

Eyal Klang is a scholar working on Health Informatics, Artificial Intelligence and Surgery. According to data from OpenAlex, Eyal Klang has authored 277 papers receiving a total of 6.3k indexed citations (citations by other indexed papers that have themselves been cited), including 68 papers in Health Informatics, 66 papers in Artificial Intelligence and 62 papers in Surgery. Recurrent topics in Eyal Klang's work include Artificial Intelligence in Healthcare and Education (68 papers), Machine Learning in Healthcare (46 papers) and Radiomics and Machine Learning in Medical Imaging (31 papers). Eyal Klang is often cited by papers focused on Artificial Intelligence in Healthcare and Education (68 papers), Machine Learning in Healthcare (46 papers) and Radiomics and Machine Learning in Medical Imaging (31 papers). Eyal Klang collaborates with scholars based in Israel, United States and United Kingdom. Eyal Klang's co-authors include Hayit Greenspan, Shelly Soffer, Michal Marianne Amitai, Yiftach Barash, Idit Diamant, Eli Konen, Jacob Goldberger, Maayan Frid-Adar, Vera Sorin and Uri Kopylov and has published in prestigious journals such as The Lancet, Nature Medicine and Journal of Clinical Oncology.

In The Last Decade

Eyal Klang

250 papers receiving 6.1k citations

Hit Papers

align trajectories

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.

GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification

2018 1.2k citations Eyal Klang et al. profile →
Convolutional Neural Networks for Radiologic Images: A Radiologist’s Guide

2019 372 citations Shelly Soffer, Eyal Klang et al. profile →
Sociodemographic biases in medical decision making by large language models

2025 27 citations Mahmud Omar, Shelly Soffer et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Eyal Klang Israel	36	1.9k	1.6k	1.2k	956	776	277	6.3k
Namkug Kim South Korea	55	4.6k 2.5×	1.2k 0.7×	423 0.4×	1.3k 1.3×	718 0.9×	412	10.9k
Justin Ko United States	22	2.6k 1.4×	3.7k 2.3×	1.3k 1.1×	481 0.5×	2.3k 3.0×	89	9.8k
Roberto A. Novoa United States	15	2.5k 1.3×	3.5k 2.2×	1.2k 1.0×	463 0.5×	2.3k 2.9×	78	8.7k
Bradley J. Erickson United States	49	4.5k 2.4×	1.8k 1.1×	1.1k 0.9×	895 0.9×	506 0.7×	280	10.4k
Kensaku Mori Japan	43	2.6k 1.4×	1.2k 0.7×	182 0.2×	1.1k 1.1×	1.5k 2.0×	399	6.7k
Andre Esteva United States	13	3.6k 1.9×	4.9k 3.0×	2.0k 1.6×	639 0.7×	2.2k 2.8×	30	11.9k
Marc Coram United States	19	2.9k 1.6×	1.2k 0.8×	648 0.5×	293 0.3×	339 0.4×	30	6.2k
Seong Ho Park South Korea	60	3.8k 2.1×	627 0.4×	577 0.5×	3.9k 4.1×	2.4k 3.1×	345	13.2k
Yuichi Mori Japan	36	1.2k 0.7×	639 0.4×	188 0.2×	1.0k 1.1×	2.7k 3.5×	222	4.4k
Jakob Nikolas Kather Germany	44	3.3k 1.8×	3.3k 2.1×	1.2k 1.0×	449 0.5×	2.1k 2.6×	214	7.7k

Countries citing papers authored by Eyal Klang

Since Specialization

Citations

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

Fields of papers citing papers by Eyal Klang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Eyal Klang

This figure shows the co-authorship network connecting the top 25 collaborators of Eyal Klang. A scholar is included among the top collaborators of Eyal Klang 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 Eyal Klang. Eyal Klang 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

Soffer, Shelly, et al.. (2025). Predictive machine-learning model for screening iron deficiency without anaemia: a retrospective cohort study. BMJ Open. 15(8). e097016–e097016.

Klang, Eyal, Benjamin S. Glicksberg, Ankit Sakhuja, et al.. (2025). Assessing Retrieval-Augmented Large Language Models for Medical Coding. NEJM AI. 2(10).

Lyden, Sean P., et al.. (2025). Advancements in imaging for endovascular treatment of peripheral artery disease. VASA.

Omar, Mahmud, Vera Sorin, Jeremy D. Collins, et al.. (2025). Multi-model assurance analysis showing large language models are highly vulnerable to adversarial hallucination attacks during clinical decision support. Communications Medicine. 5(1). 330–330. 6 indexed citations

Sorin, Vera, et al.. (2025). AI-generated videos in medical education: systematic review. BMJ Open Quality. 14(4). e003704–e003704.

Sorin, Vera, et al.. (2024). Advancing Clinical Practice: The Potential of Multimodal Technology in Modern Medicine. Journal of Clinical Medicine. 13(20). 6246–6246. 8 indexed citations

Omar, Mahmud, et al.. (2024). Generating credible referenced medical research: A comparative study of openAI's GPT-4 and Google's gemini. Computers in Biology and Medicine. 185. 109545–109545. 12 indexed citations

Patel, Dhavalkumar D., Prem Timsina, Benjamin S. Glicksberg, et al.. (2024). Traditional Machine Learning, Deep Learning, and BERT (Large Language Model) Approaches for Predicting Hospitalizations From Nurse Triage Notes: Comparative Evaluation of Resource Management. PubMed. 3. e52190–e52190. 5 indexed citations

David, Daniel, et al.. (2024). The use of artificial intelligence based chat bots in ophthalmology triage. Eye. 39(4). 785–789. 1 indexed citations

10.

Barda, Noam, Eyal Klang, Paul Fefer, et al.. (2024). Enhancing Coronary Revascularization Decisions: The Promising Role of Large Language Models as a Decision-Support Tool for Multidisciplinary Heart Team. Circulation Cardiovascular Interventions. 17(11). e014201–e014201. 4 indexed citations

11.

Omar, Mahmud, Dana Brin, Benjamin S. Glicksberg, & Eyal Klang. (2024). Utilizing natural language processing and large language models in the diagnosis and prediction of infectious diseases: A systematic review. American Journal of Infection Control. 52(9). 992–1001. 16 indexed citations

12.

Sorin, Vera, Dana Brin, Yiftach Barash, et al.. (2024). Large Language Models and Empathy: Systematic Review. Journal of Medical Internet Research. 26. e52597–e52597. 48 indexed citations

13.

Omar, Mahmud, Dana Brin, Benjamin S. Glicksberg, & Eyal Klang. (2024). Utilizing Natural Language Processing and Large Language Models in the Diagnosis and Prediction of Infectious Diseases: A Systematic Review. medRxiv. 2 indexed citations

14.

Brin, Dana, Vera Sorin, Eli Konen, et al.. (2024). How GPT models perform on the United States medical licensing examination: a systematic review. Discover Applied Sciences. 6(10). 3 indexed citations

15.

Anteby, Roi, et al.. (2023). Artificial intelligence for X-ray scaphoid fracture detection: a systematic review and diagnostic test accuracy meta-analysis. European Radiology. 34(7). 4341–4351. 13 indexed citations

16.

Primov‐Fever, Adi, Shelly Soffer, Roi Anteby, et al.. (2023). Deep learning in voice analysis for diagnosing vocal cord pathologies: a systematic review. European Archives of Oto-Rhino-Laryngology. 281(2). 863–871. 7 indexed citations

17.

Klang, Eyal, et al.. (2023). Leveraging Large Language Models to Enhance Digital Health in Cardiology: A Preview of a Cutting-Edge Language Generation Model. SHILAP Revista de lepidopterología. 1(2). 105–108. 3 indexed citations

18.

Sorin, Vera, Dana Brin, Yiftach Barash, et al.. (2023). Large Language Models and Empathy: Systematic Review (Preprint). 2 indexed citations

19.

Vaid, Akhil, Edgar Argulian, Stamatios Lerakis, et al.. (2023). Multi-center retrospective cohort study applying deep learning to electrocardiograms to identify left heart valvular dysfunction. SHILAP Revista de lepidopterología. 3(1). 24–24. 15 indexed citations

20.

Klang, Eyal, et al.. (2023). Advancing radiology practice and research: harnessing the potential of large language models amidst imperfections. BJR|Open. 6(1). tzae022–tzae022. 2 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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