Artificial Intelligence in Medicine

2.3k papers and 63.5k indexed citations i.

About

The 2.3k papers published in Artificial Intelligence in Medicine in the last decades have received a total of 63.5k indexed citations. Papers published in Artificial Intelligence in Medicine usually cover Artificial Intelligence (1.1k papers), Molecular Biology (586 papers) and Radiology, Nuclear Medicine and Imaging (315 papers) specifically the topics of Biomedical Text Mining and Ontologies (339 papers), Machine Learning in Healthcare (249 papers) and Semantic Web and Ontologies (186 papers). The most active scholars publishing in Artificial Intelligence in Medicine are Igor Kononenko, Ann E. Smith, A. E. Eiben, Silvana Quaglini, Yuval Shaḥar, Nada Lavrač, Rudy Setiono, Dursun Delen, Remco C. Veltkamp and Mario Stefanelli.

In The Last Decade

Fields of papers published in Artificial Intelligence in Medicine

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers published in Artificial Intelligence in Medicine. 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 Artificial Intelligence in Medicine.

Countries where authors publish in Artificial Intelligence in Medicine

Since Specialization
Citations

This map shows the geographic impact of research published in Artificial Intelligence in Medicine. 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 Artificial Intelligence in Medicine 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 in Medicine 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