Natalie Shih

44 papers and 2.4k indexed citations i.

About

Natalie Shih is a scholar working on Artificial Intelligence, Pulmonary and Respiratory Medicine and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Natalie Shih has authored 44 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Artificial Intelligence, 14 papers in Pulmonary and Respiratory Medicine and 14 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Natalie Shih’s work include AI in cancer detection (16 papers), Radiomics and Machine Learning in Medical Imaging (13 papers) and Prostate Cancer Diagnosis and Treatment (9 papers). Natalie Shih is often cited by papers focused on AI in cancer detection (16 papers), Radiomics and Machine Learning in Medical Imaging (13 papers) and Prostate Cancer Diagnosis and Treatment (9 papers). Natalie Shih collaborates with scholars based in United States, The Netherlands and Colombia. Natalie Shih's co-authors include Michael D. Feldman, Anant Madabhushi, John Tomaszewski, Ajay Basavanhally, Hannah Gilmore, Ángel Cruz-Roa, Fabio A. González, Shridar Ganesan, Zhi Wei and Sagarika Banerjee and has published in prestigious journals such as Journal of Clinical Investigation, Journal of Clinical Oncology and Blood.

In The Last Decade

Co-authorship network of co-authors of Natalie Shih i

Fields of papers citing papers by Natalie Shih

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Natalie Shih. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Natalie Shih. The network helps show where Natalie Shih may publish in the future.

Countries citing papers authored by Natalie Shih

Since Specialization
Citations

This map shows the geographic impact of Natalie Shih's research. 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 Natalie Shih with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Natalie Shih 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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