Michael Eppler

726 total citations
18 papers, 310 citations indexed

About

Michael Eppler is a scholar working on Health Informatics, Surgery and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, Michael Eppler has authored 18 papers receiving a total of 310 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Health Informatics, 6 papers in Surgery and 5 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in Michael Eppler's work include Artificial Intelligence in Healthcare and Education (8 papers), Cardiac, Anesthesia and Surgical Outcomes (4 papers) and Radiomics and Machine Learning in Medical Imaging (2 papers). Michael Eppler is often cited by papers focused on Artificial Intelligence in Healthcare and Education (8 papers), Cardiac, Anesthesia and Surgical Outcomes (4 papers) and Radiomics and Machine Learning in Medical Imaging (2 papers). Michael Eppler collaborates with scholars based in United States, United Kingdom and Japan. Michael Eppler's co-authors include Giovanni Cacciamani, Inderbir S. Gill, Conner Ganjavi, Andre Luis Abreu, Lorenzo Storino Ramacciotti, Gary S. Collins, Mary K. Samplaski, Jeffrey Loh-Doyle, Ryan J. Davis and Mihir Desai and has published in prestigious journals such as SHILAP Revista de lepidopterología, Annals of Surgery and BMJ.

In The Last Decade

Michael Eppler

17 papers receiving 303 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michael Eppler United States 8 214 85 57 57 34 18 310
Alexander Pan United States 7 156 0.7× 74 0.9× 78 1.4× 59 1.0× 25 0.7× 14 267
Bryan Lim Australia 9 159 0.7× 58 0.7× 39 0.7× 50 0.9× 71 2.1× 35 279
Sebastian Fritsch Germany 6 152 0.7× 77 0.9× 34 0.6× 64 1.1× 28 0.8× 27 327
Nitin Srinivasan United States 5 150 0.7× 57 0.7× 57 1.0× 56 1.0× 51 1.5× 15 240
Nithya Rajeev United States 5 150 0.7× 58 0.7× 56 1.0× 56 1.0× 46 1.4× 10 227
Joseph Weisberger United States 8 215 1.0× 48 0.6× 40 0.7× 97 1.7× 75 2.2× 24 355
Isabel Herzog United States 7 232 1.1× 50 0.6× 41 0.7× 101 1.8× 56 1.6× 25 314
Víctor Díaz‐Flores García Spain 8 209 1.0× 61 0.7× 31 0.5× 100 1.8× 10 0.3× 25 352
Patricia L. Zadnik Sullivan United States 3 292 1.4× 90 1.1× 24 0.4× 167 2.9× 34 1.0× 5 353
Jevan Cevik Australia 8 112 0.5× 43 0.5× 29 0.5× 38 0.7× 80 2.4× 35 237

Countries citing papers authored by Michael Eppler

Since Specialization
Citations

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

Fields of papers citing papers by Michael Eppler

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michael Eppler

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

All Works

18 of 18 papers shown
1.
Ganjavi, Conner, Michael Eppler, Severin Rodler, et al.. (2025). Generative artificial intelligence in oncology. Current Opinion in Urology. 35(3). 205–213.
2.
Ganjavi, Conner, Michael Eppler, Lorenzo Storino Ramacciotti, et al.. (2024). ChatGPT and large language models (LLMs) awareness and use. A prospective cross-sectional survey of U.S. medical students. SHILAP Revista de lepidopterología. 3(9). e0000596–e0000596. 24 indexed citations
3.
Eppler, Michael, Severin Rodler, Lorenzo Storino Ramacciotti, et al.. (2024). Accuracy, readability, and understandability of large language models for prostate cancer information to the public. Prostate Cancer and Prostatic Diseases. 28(2). 394–399. 37 indexed citations
4.
Ganjavi, Conner, Michael Eppler, Andre Luis Abreu, et al.. (2024). Publishers’ and journals’ instructions to authors on use of generative artificial intelligence in academic and scientific publishing: bibliometric analysis. BMJ. 384. e077192–e077192. 74 indexed citations
5.
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Eppler, Michael, Tamir Sholklapper, Mitchell G. Goldenberg, et al.. (2023). Severity Grading Systems for Intraoperative Adverse Events. A Systematic Review of the Literature and Citation Analysis. Annals of Surgery. 278(5). e973–e980. 6 indexed citations
7.
Ganjavi, Conner, Michael Eppler, Lorenzo Storino Ramacciotti, & Giovanni Cacciamani. (2023). Clinical Patient Summaries Not Fit for Purpose: A Study in Urology. European Urology Focus. 9(6). 1068–1071. 13 indexed citations
8.
Eppler, Michael, Conner Ganjavi, Ryan J. Davis, et al.. (2023). Bridging the Gap Between Urological Research and Patient Understanding: The Role of Large Language Models in Automated Generation of Layperson’s Summaries. Urology Practice. 10(5). 436–443. 38 indexed citations
9.
Eppler, Michael, Marissa Maas, Mihir Desai, et al.. (2023). Automated Capture of Intraoperative Adverse Events Using Artificial Intelligence: A Systematic Review and Meta-Analysis. Journal of Clinical Medicine. 12(4). 1687–1687. 19 indexed citations
10.
Ramacciotti, Lorenzo Storino, Masatomo Kaneko, Michael Eppler, et al.. (2023). Comprehensive Assessment of MRI-based Artificial Intelligence Frameworks Performance in the Detection, Segmentation, and Classification of Prostate Lesions Using Open-Source Databases. Urologic Clinics of North America. 51(1). 131–161. 1 indexed citations
11.
Eppler, Michael, Nikhil Singh, Li Ding, Gregory A. Magee, & Parveen K. Garg. (2023). Discharge prescription patterns for antiplatelet and statin therapy following carotid endarterectomy: an analysis of the vascular quality initiative. BMJ Open. 13(7). e071550–e071550. 1 indexed citations
12.
Cacciamani, Giovanni, Michael Eppler, Tamir Sholklapper, et al.. (2023). Recommendations for Intraoperative Adverse Events Data Collection in Clinical Studies and Study Protocols. An ICARUS Global Surgical Collaboration Study. PubMed. 27(1). 23–83. 5 indexed citations
13.
Eppler, Michael, et al.. (2023). The Benefits and Dangers of Artificial Intelligence in Healthcare Research Writing. 7(1). 1–2. 5 indexed citations
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16.
Cacciamani, Giovanni, Afsaneh Barzi, Michael Eppler, et al.. (2022). The Impact of Facility Surgical Caseload Volumes on Survival Outcomes in Patients Undergoing Radical Cystectomy. Cancers. 14(23). 5984–5984. 2 indexed citations
17.
Eppler, Michael, Ioanna K. Bolia, James E. Tibone, et al.. (2021). Superior Capsular Reconstruction of the Shoulder. Arthroscopy The Journal of Arthroscopic and Related Surgery. 37(6). 1708–1710. 1 indexed citations
18.
Müller, Eilif, Andrew P. Davison, Michael Eppler, et al.. (2009). NeuralEnsemble.Org: Unifying neural simulators in Python toease the model complexity bottleneck. Frontiers in Neuroinformatics. 3. 5 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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2026