Radiology Artificial Intelligence

339 papers and 6.9k indexed citations i.

About

The 339 papers published in Radiology Artificial Intelligence in the last decades have received a total of 6.9k indexed citations. Papers published in Radiology Artificial Intelligence usually cover Radiology, Nuclear Medicine and Imaging (263 papers), Health Informatics (93 papers) and Biomedical Engineering (83 papers) specifically the topics of Radiomics and Machine Learning in Medical Imaging (198 papers), Artificial Intelligence in Healthcare and Education (93 papers) and AI in cancer detection (58 papers). The most active scholars publishing in Radiology Artificial Intelligence are Bradley J. Erickson, Felipe Kitamura, Carl Sabottke, Bradley Spieler, Alice Yu, John Eng, Bahram Mohajer, Jayashree Kalpathy‐Cramer, Ronald M. Summers and Luciano M. Prevedello.

In The Last Decade

Fields of papers published in Radiology Artificial Intelligence

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Countries where authors publish in Radiology Artificial Intelligence

Since Specialization
Citations

This map shows the geographic impact of research published in Radiology 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 Radiology 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 Radiology 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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