Daniel L. Recla

7 papers and 326 indexed citations i.

About

Daniel L. Recla is a scholar working on Artificial Intelligence, Physiology and Public Health, Environmental and Occupational Health. According to data from OpenAlex, Daniel L. Recla has authored 7 papers receiving a total of 326 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 4 papers in Physiology and 3 papers in Public Health, Environmental and Occupational Health. Recurrent topics in Daniel L. Recla’s work include AI in cancer detection (7 papers), Clinical Laboratory Practices and Quality Control (4 papers) and Telemedicine and Telehealth Implementation (3 papers). Daniel L. Recla is often cited by papers focused on AI in cancer detection (7 papers), Clinical Laboratory Practices and Quality Control (4 papers) and Telemedicine and Telehealth Implementation (3 papers). Daniel L. Recla collaborates with scholars based in United States. Daniel L. Recla's co-authors include Bruce E. Dunn, Hongyung Choi, Urias A. Almagro, Ronald S. Weinstein, Elizabeth A. Krupinski, Sarah E. Kerr, Anna R. Graham, Neela K. Sheth and Craig W. Davis and has published in prestigious journals such as Human Pathology, Telemedicine Journal and e-Health and Seminars in Diagnostic Pathology.

In The Last Decade

Co-authorship network of co-authors of Daniel L. Recla i

Fields of papers citing papers by Daniel L. Recla

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel L. Recla. 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 Daniel L. Recla. The network helps show where Daniel L. Recla may publish in the future.

Countries citing papers authored by Daniel L. Recla

Since Specialization
Citations

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