Frontiers in Artificial Intelligence

1.1k papers and 9.6k indexed citations i.

About

The 1.1k papers published in Frontiers in Artificial Intelligence in the last decades have received a total of 9.6k indexed citations. Papers published in Frontiers in Artificial Intelligence usually cover Artificial Intelligence (502 papers), Radiology, Nuclear Medicine and Imaging (131 papers) and Health Informatics (113 papers) specifically the topics of Artificial Intelligence in Healthcare and Education (113 papers), Topic Modeling (106 papers) and COVID-19 diagnosis using AI (70 papers). The most active scholars publishing in Frontiers in Artificial Intelligence are Paolo Giudici, Tirth Dave, Frédéric Chazal, Bertrand Michel, Frank Emmert‐Streib, Matthias Dehmer, Shailesh Tripathi, Feng Han, Zhen Yang and Adam Safron.

In The Last Decade

Fields of papers published in Frontiers in Artificial Intelligence

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers published in Frontiers in Artificial Intelligence. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers published in Frontiers in Artificial Intelligence.

Countries where authors publish in Frontiers in Artificial Intelligence

Since Specialization
Citations

This map shows the geographic impact of research published in Frontiers in Artificial Intelligence. 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 papers published in Frontiers in Artificial Intelligence with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Frontiers in Artificial Intelligence more than expected).

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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2025