Gregor Štiglic

120 papers receiving 1.7k citations

Gregor Štiglic's Hit Papers

The ChatGPT effect and transforming nursing education with generative AI: Discussion paper 2024 · 48 citations
480+2+4Years since publication50100150200

Peers

Gregor Štiglic
Comparison fields: 5 of 157
  • Health Informatics 130
  • Health Information Management 238
  • Research and Theory 19
  • Issues, ethics and legal aspects 18
  • Artificial Intelligence 393
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Anne Moen Norway
Nabil Zary Sweden
Leanne M. Currie Canada
Rebecca Randell United Kingdom
Onur Asan United States
Thomas Kannampallil United States
Charlene H. Chu Canada
Niall Higgins Australia
Maxim Topaz United States
Trevor Cohen United States
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Countries citing papers authored by Gregor Štiglic

Since Specialization
Citations

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

Fields of papers citing papers by Gregor Štiglic

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Gregor Štiglic, 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 Gregor Štiglic Line = papers co-authored together Gregor Štiglic links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 132 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Early detection of type 2 diabetes mellitus using machine learning-based prediction models
Hit paper breakdown →
2020234
2 201278
3 202177
4 201865
5 201553
6 202051
7 202051
8
The ChatGPT effect and transforming nursing education with generative AI: Discussion paper
Hit paper breakdown →
202448
9 201747
10 202442
11 201036
12 202036
13 202236
14 201931
15 201631
16 201729
17 201928
18 201726
19 200926
20 202324

About Gregor Štiglic

Gregor Štiglic is a scholar working on Artificial Intelligence, General Health Professions, Health Information Management, Molecular Biology and Information Systems, having authored 132 papers that have together received 1.8k indexed citations. Recurring topics across this work include Machine Learning in Healthcare (12 papers), Artificial Intelligence in Healthcare (12 papers), Gene expression and cancer classification (9 papers), Mobile Health and mHealth Applications (8 papers), Artificial Intelligence in Healthcare and Education (7 papers), Data Mining Algorithms and Applications (6 papers), Evolutionary Algorithms and Applications (5 papers) and Nursing Diagnosis and Documentation (5 papers). The work is most often cited by research in Health Informatics (130 citations), Health Information Management (238 citations), Research and Theory (19 citations), Issues, ethics and legal aspects (18 citations) and Artificial Intelligence (393 citations). Gregor Štiglic has collaborated with scholars based in Slovenia, United Kingdom and United States. Frequent co-authors include Leona Cilar, Majda Pajnkihar, Peter Kokol, Primož Kocbek, Aziz Sheikh, Dominika Vrbnjak, Lucija Gosak, Leon Kopitar, Nino Fijačko and Katrien Verbert. Their work appears in journals such as Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, Journal of Medical Internet Research, PLoS ONE, JMIR mhealth and uhealth and Nurse Education in Practice.

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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