Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Future of Artificial Intelligence—Machine Learning Trends in Pathology and Medicine
202540 citationsMatthew G. Hanna, Rajesh Dash et al.Modern Pathologyprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by James H. Harrison
Since
Specialization
Citations
This map shows the geographic impact of James H. Harrison'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 James H. Harrison with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites James H. Harrison more than expected).
Fields of papers citing papers by James H. Harrison
This network shows the impact of papers produced by James H. Harrison. 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 James H. Harrison. The network helps show where James H. Harrison may publish in the future.
Co-authorship network of co-authors of James H. Harrison
This figure shows the co-authorship network connecting the top 25 collaborators of James H. Harrison.
A scholar is included among the top collaborators of James H. Harrison based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with James H. Harrison. James H. Harrison is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Hanna, Matthew G., Rajesh Dash, James H. Harrison, et al.. (2025). Future of Artificial Intelligence—Machine Learning Trends in Pathology and Medicine. Modern Pathology. 38(4). 100705–100705.40 indexed citations breakdown →
Henricks, Walter H., Donald S. Karcher, James H. Harrison, et al.. (2016). Pathology Informatics Essentials for Residents. Academic Pathology. 3. 1530659179–1530659179.8 indexed citations
Siadaty, Mir S. & James H. Harrison. (2008). Multi-Database Mining. Clinics in Laboratory Medicine. 28(1). 73–82.1 indexed citations
15.
Harrison, James H., et al.. (2007). Abstraction-based temporal data retrieval for a Clinical Data Repository.. PubMed. 603–7.9 indexed citations
16.
Lewis, Deborah, et al.. (2005). Caring Connection: developing an Internet resource for family caregivers of children with cancer.. PubMed. 1026–1026.10 indexed citations
17.
Mitchell, Kevin J., Michael J. Becich, Jules J. Berman, et al.. (2004). Implementation and evaluation of a negation tagger in a pipeline-based system for information extract from pathology reports.. PubMed. 107(Pt 1). 663–7.43 indexed citations
18.
Harrison, James H., et al.. (2002). An Enhanced Framework for Pattern Detection in Clinical Laboratory Data.. PubMed Central. 1134–1134.2 indexed citations
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.