Michael Wornow

1.2k citations
13 papers · 484 indexed · 1 hit paper · h-index 7

Impact in

Papers in

Michael Wornow

11 papers receiving 478 citations

Hit Papers

Testing and Evaluation of Health Care Applications of Large Language Models 2024 · 137 citations
1372024202620254080120

Peers

Michael Wornow
Comparison fields: 5 of 94
  • Health Informatics 179
  • Health Information Management 45
  • Sensory Systems 40
  • Family Practice 18
  • Artificial Intelligence 167
Replace David Wong with:
David Wong United Kingdom
Gergő Bohner United Kingdom
Yaara Goldschmidt Israel
Shubo Tian United States
Brenda Y. Miao United States
Jean-Benoit Delbrouck United States
Arya Rao United States
Ashwin Nayak United States
Sune Holm Denmark
Aaron Casey Australia
Michael Wornow relative to David Wong United Kingdom David Wong's profile →
Citations per field
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Citations per year

Countries citing papers authored by Michael Wornow

Since Specialization
Citations

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

Fields of papers citing papers by Michael Wornow

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Michael Wornow, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Michael Wornow Line = papers co-authored together Michael Wornow links everyone, so they are left out of the graph.

All Works

13 of 13 papers shown
#Work
1 202415
2 202416
3
Testing and Evaluation of Health Care Applications of Large Language Models
Hit paper breakdown →
2024137
4 20242
5 20240
6 20239
7 2023143
8 20230
9 20224
10 202215
11
Cut out the annotator, keep the cutout: better segmentation with weak supervision
20213
12 20205
13 2020135

About Michael Wornow

Michael Wornow is a scholar working on Health Informatics, Health Information Management, Management Information Systems, Emergency Medical Services and Artificial Intelligence, having authored 13 papers that have together received 484 indexed citations. Recurring topics across this work include Machine Learning in Healthcare (3 papers), Artificial Intelligence in Healthcare and Education (3 papers), Radiomics and Machine Learning in Medical Imaging (2 papers), Artificial Intelligence in Healthcare (2 papers), Business Process Modeling and Analysis (2 papers), RNA and protein synthesis mechanisms (2 papers), interferon and immune responses (1 paper) and Clinical practice guidelines implementation (1 paper). The work is most often cited by research in Health Informatics (179 citations), Health Information Management (45 citations), Sensory Systems (40 citations), Family Practice (18 citations) and Artificial Intelligence (167 citations). Michael Wornow has collaborated with scholars based in United States, Australia and Thailand. Frequent co-authors include Nigam H. Shah, Michael A. Pfeffer, Jason Fries, Scott L. Fleming, Birju Patel, Yizhe Xu, Rahul Thapa, Ethan Steinberg, Wei-Hsi Yeh and David R. Liu. Their work appears in journals such as JAMA, Clinical Infectious Diseases, PLoS Computational Biology, npj Digital Medicine and Nature Communications.

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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