Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge
- Journal
- UTUPub (University of Turku)
In The Last Decade
doi.org/w10261121 →Countries where authors are citing Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge
This map shows the geographic impact of Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge. 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 Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge 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 for diagnosis and Gleason grading of prostate cancer: the PANDA challenge more than expected).
Fields of papers citing Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge
This network shows the impact of Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge.
About Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge
This paper, published in 2022, received 262 indexed citations . Written by Hester van Boven, Christina Hulsbergen‐van de Kaa and Greg S. Corrado covering the research area of Health Informatics, Pulmonary and Respiratory Medicine and Radiology, Nuclear Medicine and Imaging. It is primarily cited by scholars working on Artificial Intelligence (197 citations), Radiology, Nuclear Medicine and Imaging (141 citations) and Pulmonary and Respiratory Medicine (75 citations). Published in UTUPub (University of Turku).
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.
This paper is also available at doi.org/w10261121.