Nature Machine Intelligence

883 papers and 37.5k indexed citations i.

About

The 883 papers published in Nature Machine Intelligence in the last decades have received a total of 37.5k indexed citations. Papers published in Nature Machine Intelligence usually cover Artificial Intelligence (297 papers), Molecular Biology (227 papers) and Computational Theory and Mathematics (110 papers) specifically the topics of Computational Drug Discovery Methods (92 papers), Machine Learning in Materials Science (88 papers) and Ethics and Social Impacts of AI (63 papers). The most active scholars publishing in Nature Machine Intelligence are Cynthia Rudin, Marcello Ienca, Effy Vayena, Anna Jobin, Brent Mittelstadt, Su‐In Lee, Alex J. DeGrave, Scott Lundberg, Hugh Chen and Gabriel Erion.

In The Last Decade

Fields of papers published in Nature Machine Intelligence

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers published in Nature Machine 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 Nature Machine Intelligence.

Countries where authors publish in Nature Machine Intelligence

Since Specialization
Citations

This map shows the geographic impact of research published in Nature Machine 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 Nature Machine Intelligence with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nature Machine 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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