Artificial Intelligence

2.0M papers and 39.2M indexed citations i.

About

2.0M papers covering Artificial Intelligence have received a total of 39.2M indexed citations since 1950. Papers on subfields are most often about the specific topic of Geochemistry and Geologic Mapping, Quantum Information and Cryptography and Neural Networks and Applications and also cover the fields of Information Systems, Computer Vision and Pattern Recognition and Computer Networks and Communications. Papers citing papers on subfields are usually about Computer Vision and Pattern Recognition, Atomic and Molecular Physics, and Optics and Electrical and Electronic Engineering. Some of the most active scholars covering Artificial Intelligence are Leo Breiman, Geoffrey E. Hinton, Vladimir Vapnik, Stephen F. Altschul, Joseph Felsenstein, L. A. Zadeh, Yoshua Bengio, Jürgen Schmidhuber, Hirotugu Akaike and Kaiming He.

In The Last Decade

Fields of papers citing papers about Artificial Intelligence

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers covering 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 covering Artificial Intelligence.

Countries where authors publish papers about Artificial Intelligence

Since Specialization

Citations

This map shows the geographic impact of research 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 about 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 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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