Mike Schaekermann

2.7k total citations
24 papers, 409 citations indexed

About

Mike Schaekermann is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Ophthalmology. According to data from OpenAlex, Mike Schaekermann has authored 24 papers receiving a total of 409 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 6 papers in Radiology, Nuclear Medicine and Imaging and 5 papers in Ophthalmology. Recurrent topics in Mike Schaekermann's work include Retinal Imaging and Analysis (5 papers), Retinal Diseases and Treatments (4 papers) and Mobile Crowdsensing and Crowdsourcing (3 papers). Mike Schaekermann is often cited by papers focused on Retinal Imaging and Analysis (5 papers), Retinal Diseases and Treatments (4 papers) and Mobile Crowdsensing and Crowdsourcing (3 papers). Mike Schaekermann collaborates with scholars based in United States, Canada and Australia. Mike Schaekermann's co-authors include Edith Law, Kate Larson, Rory Sayres, Andrew Lim, Abigail E. Huang, Joslin Goh, Dale R. Webster, Matthew Lease, Yun Liu and Jonathan Krause and has published in prestigious journals such as Neurology, Ophthalmology and Investigative Ophthalmology & Visual Science.

In The Last Decade

Mike Schaekermann

23 papers receiving 397 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mike Schaekermann United States 13 159 145 129 52 51 24 409
Elizabeth Baylor Thailand 3 114 0.7× 119 0.8× 59 0.5× 20 0.4× 5 0.1× 5 327
Benson S. Y. Lam Hong Kong 8 149 0.9× 72 0.5× 109 0.8× 190 3.7× 25 0.5× 27 440
Orazio Gambino Italy 11 122 0.8× 88 0.6× 15 0.1× 142 2.7× 4 0.1× 39 358
Kathy Clawson United Kingdom 9 69 0.4× 93 0.6× 18 0.1× 108 2.1× 24 0.5× 20 287
Eugene Tseytlin United States 15 133 0.8× 341 2.4× 5 0.0× 18 0.3× 30 0.6× 27 692
Muhammad Mujahid Saudi Arabia 10 92 0.6× 257 1.8× 4 0.0× 59 1.1× 25 0.5× 32 508
Eugenio Alberdi United Kingdom 10 69 0.4× 171 1.2× 6 0.0× 37 0.7× 4 0.1× 20 456
Stefano Cirillo Italy 11 29 0.2× 144 1.0× 5 0.0× 35 0.7× 8 0.2× 49 381
Gregg Willcox United States 7 43 0.3× 79 0.5× 3 0.0× 19 0.4× 17 0.3× 22 252
Michael Tang United States 9 103 0.6× 54 0.4× 53 0.4× 85 1.6× 11 0.2× 20 242

Countries citing papers authored by Mike Schaekermann

Since Specialization
Citations

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

Fields of papers citing papers by Mike Schaekermann

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mike Schaekermann

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

All Works

20 of 20 papers shown
1.
Wong, Renee, Alan Karthikesalingam, Yossi Matias, et al.. (2025). Generative AI for medical education: Insights from a case study with medical students and an AI tutor for clinical reasoning. 1–8. 1 indexed citations
2.
Stutz, David, Ali Taylan Cemgil, Abhijit Guha Roy, et al.. (2025). Evaluating medical AI systems in dermatology under uncertain ground truth. Medical Image Analysis. 103. 103556–103556.
3.
4.
Aroyo, Lora, Matthew Lease, Praveen Paritosh, & Mike Schaekermann. (2022). Data excellence for AI. interactions. 29(2). 66–69. 16 indexed citations
5.
Hettiachchi, Danula, et al.. (2021). The Challenge of Variable Effort Crowdsourcing and How Visible Gold Can Help. arXiv (Cornell University). 13 indexed citations
6.
Hettiachchi, Danula, Mark Sanderson, Jorge Gonçalves, et al.. (2021). Investigating and Mitigating Biases in Crowdsourced Data. 331–334. 10 indexed citations
7.
Soonthornworasiri, Ngamphol, Chetan Rao, Rajiv Raman, et al.. (2020). Longitudinal Screening for Diabetic Retinopathy in a Nationwide Screening Program: Comparing Deep Learning and Human Graders. Journal of Diabetes Research. 2020. 1–8. 12 indexed citations
8.
Schaekermann, Mike, et al.. (2020). Ambiguity-aware AI Assistants for Medical Data Analysis. 1–14. 31 indexed citations
9.
Schaekermann, Mike, Carrie J. Cai, Abigail E. Huang, & Rory Sayres. (2020). Expert Discussions Improve Comprehension of Difficult Cases in Medical Image Assessment. 1–13. 18 indexed citations
10.
Hammel, Naama, Mike Schaekermann, Sonia Phene, et al.. (2019). A Study of Feature-based Consensus Formation for Glaucoma Risk Assessment. Investigative Ophthalmology & Visual Science. 60(9). 164–164. 1 indexed citations
11.
Cohen, Robin, et al.. (2019). Trusted AI and the Contribution of Trust Modeling in Multiagent Systems. Adaptive Agents and Multi-Agents Systems. 1644–1648. 6 indexed citations
12.
Schaekermann, Mike, Naama Hammel, Bilson Campana, et al.. (2019). Asynchronous Remote Adjudication for Grading Diabetic Retinopathy. Investigative Ophthalmology & Visual Science. 60(9). 158–158. 1 indexed citations
13.
Phene, Sonia, Naama Hammel, Yun Liu, et al.. (2019). Deep Learning and Glaucoma Specialists. Ophthalmology. 126(12). 1627–1639. 126 indexed citations
14.
Williams, Jennifer A., Mike Schaekermann, Aissatou Kenda Bah, et al.. (2019). Smartphone EEG and remote online interpretation for children with epilepsy in the Republic of Guinea: Quality, characteristics, and practice implications. Seizure. 71. 93–99. 24 indexed citations
15.
Schaekermann, Mike, et al.. (2019). Capturing Expert Arguments from Medical Adjudication Discussions in a Machine-readable Format. 1131–1137. 5 indexed citations
16.
Schaekermann, Mike, et al.. (2019). Understanding Expert Disagreement in Medical Data Analysis through Structured Adjudication. Proceedings of the ACM on Human-Computer Interaction. 3(CSCW). 1–23. 30 indexed citations
17.
Williams, Jennifer S., Mike Schaekermann, Aissatou Kenda Bah, et al.. (2019). Utilizing a wearable smartphone-based EEG for pediatric epilepsy patients in the resource poor environment of Guinea: A prospective study. (N5.001). Neurology. 92(15_supplement). 1 indexed citations
18.
Schaekermann, Mike, Joslin Goh, Kate Larson, & Edith Law. (2018). Resolvable vs. Irresolvable Disagreement. Proceedings of the ACM on Human-Computer Interaction. 2(CSCW). 1–19. 40 indexed citations
19.
Jaini, Priyank, Zhitang Chen, Edith Law, et al.. (2017). Online Bayesian Transfer Learning for Sequential Data Modeling. International Conference on Learning Representations. 11 indexed citations
20.
Schaekermann, Mike, Günter Wallner, Simone Kriglstein, et al.. (2017). Curiously Motivated. University of Southern Denmark Research Portal (University of Southern Denmark). 143–156. 16 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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