Matthew G. Hanna

36.1k total citations · 4 hit papers
68 papers, 20.3k citations indexed

About

Matthew G. Hanna is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Oncology. According to data from OpenAlex, Matthew G. Hanna has authored 68 papers receiving a total of 20.3k indexed citations (citations by other indexed papers that have themselves been cited), including 49 papers in Artificial Intelligence, 18 papers in Radiology, Nuclear Medicine and Imaging and 15 papers in Oncology. Recurrent topics in Matthew G. Hanna's work include AI in cancer detection (46 papers), Radiomics and Machine Learning in Medical Imaging (16 papers) and Cell Image Analysis Techniques (14 papers). Matthew G. Hanna is often cited by papers focused on AI in cancer detection (46 papers), Radiomics and Machine Learning in Medical Imaging (16 papers) and Cell Image Analysis Techniques (14 papers). Matthew G. Hanna collaborates with scholars based in United States, Australia and Sweden. Matthew G. Hanna's co-authors include Andrew Kernytsky, David Altshuler, Eric Banks, Kiran Garimella, Mark A. DePristo, Mark J. Daly, Andrey Sivachenko, Kristian Cibulskis, Stacey Gabriel and Aaron McKenna and has published in prestigious journals such as Nature Medicine, Journal of Clinical Oncology and SHILAP Revista de lepidopterología.

In The Last Decade

Matthew G. Hanna

67 papers receiving 20.1k citations

Hit Papers

align trajectories

What are hit papers?

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.

The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data

2010 17.4k citations Aaron McKenna, Matthew G. Hanna et al. Genome Research profile →
Clinical-grade computational pathology using weakly supervised deep learning on whole slide images

2019 1.4k citations Matthew G. Hanna, Luke Geneslaw et al. profile →
Ethical and Bias Considerations in Artificial Intelligence/Machine Learning

2024 121 citations Matthew G. Hanna, Liron Pantanowitz et al. Modern Pathology profile →
Future of Artificial Intelligence—Machine Learning Trends in Pathology and Medicine

2025 40 citations Matthew G. Hanna, Mustafa Deebajah et al. Modern Pathology profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Matthew G. Hanna United States	22	8.5k	7.1k	3.5k	2.8k	2.1k	68	20.3k
Ryan Poplin United States	8	6.7k 0.8×	5.3k 0.7×	1.8k 0.5×	2.0k 0.7×	515 0.2×	12	14.1k
Simon Tavaré United States	68	10.7k 1.3×	6.7k 0.9×	1.8k 0.5×	3.6k 1.3×	1.7k 0.8×	220	21.3k
Andrew Kasarskis United States	31	22.8k 2.7×	4.3k 0.6×	3.5k 1.0×	3.4k 1.2×	1.5k 0.7×	80	33.9k
Paul T. Spellman United States	40	21.9k 2.6×	3.1k 0.4×	2.3k 0.7×	3.9k 1.4×	1.9k 0.9×	114	31.1k
Allan Peter Davis United States	32	25.8k 3.0×	4.4k 0.6×	3.5k 1.0×	3.7k 1.4×	2.4k 1.2×	55	36.5k
Selina S. Dwight United States	13	23.2k 2.7×	4.0k 0.6×	3.6k 1.0×	3.3k 1.2×	1.5k 0.7×	20	32.2k
Judith A. Blake United States	48	26.4k 3.1×	4.9k 0.7×	3.7k 1.1×	3.7k 1.3×	2.3k 1.1×	137	36.9k
Martin Ringwald United States	29	24.5k 2.9×	4.2k 0.6×	3.5k 1.0×	3.4k 1.2×	1.8k 0.8×	57	33.8k
David P. Hill United States	28	23.6k 2.8×	4.1k 0.6×	3.6k 1.0×	3.4k 1.2×	1.8k 0.9×	55	33.3k
Joel E. Richardson United States	35	24.3k 2.9×	4.6k 0.7×	3.6k 1.0×	3.5k 1.3×	2.0k 0.9×	81	34.7k

Countries citing papers authored by Matthew G. Hanna

Since Specialization

Citations

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

Fields of papers citing papers by Matthew G. Hanna

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Matthew G. Hanna. 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 Matthew G. Hanna. The network helps show where Matthew G. Hanna may publish in the future.

Co-authorship network of co-authors of Matthew G. Hanna

This figure shows the co-authorship network connecting the top 25 collaborators of Matthew G. Hanna. A scholar is included among the top collaborators of Matthew G. Hanna 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 Matthew G. Hanna. Matthew G. Hanna is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Elishaev, Esther, Rohit Bhargava, Liron Pantanowitz, et al.. (2025). Assessment of the efficacy and accuracy of cervical cytology screening with the Hologic Genius Digital Diagnostics System. Cancer Cytopathology. 133(7). e70022–e70022. 1 indexed citations

Elishaev, Esther, Rohit Bhargava, Matthew G. Hanna, et al.. (2025). Diagnostic performance of the hologic genius digital diagnostics system for low-grade squamous intraepithelial lesion (LSIL) ThinPrep papanicolaou tests. Journal of the American Society of Cytopathology. 14(3). 199–207. 2 indexed citations

Bauer, Thomas W., Matthew G. Hanna, Kelly D. Smith, et al.. (2024). A multicenter study to evaluate the analytical precision by pathologists using the Aperio GT 450 DX. Journal of Pathology Informatics. 15. 100401–100401. 1 indexed citations

García, Víctor, Amy Ly, Evangelos Hytopoulos, et al.. (2024). Reproducible Reporting of the Collection and Evaluation of Annotations for Artificial Intelligence Models. Modern Pathology. 37(4). 100439–100439. 6 indexed citations

Hanna, Matthew G., Liron Pantanowitz, Brian Jackson, et al.. (2024). Ethical and Bias Considerations in Artificial Intelligence/Machine Learning. Modern Pathology. 38(3). 100686–100686. 121 indexed citations breakdown →

Patel, Ankush, Matthew G. Hanna, Jochen K. Lennerz, et al.. (2024). Bridging the Clinical-Computational Transparency Gap in Digital Pathology. Archives of Pathology & Laboratory Medicine. 149(3). 276–287. 2 indexed citations

Pantanowitz, Liron, Thomas M. Pearce, Matthew G. Hanna, et al.. (2024). Nongenerative Artificial Intelligence in Medicine: Advancements and Applications in Supervised and Unsupervised Machine Learning. Modern Pathology. 38(3). 100680–100680. 17 indexed citations

Hanna, Matthew G. & Orly Ardon. (2023). Digital pathology systems enabling quality patient care. Genes Chromosomes and Cancer. 62(11). 685–697. 13 indexed citations

Ardon, Orly, Maria A. Friedlander, Lorraine Corsale, et al.. (2023). Quality Management System in Clinical Digital Pathology Operations at a Tertiary Cancer Center. Laboratory Investigation. 103(11). 100246–100246. 10 indexed citations

10.

Ardon, Orly, Eric A. Klein, Lorraine Corsale, et al.. (2023). Digital pathology operations at a tertiary cancer center: Infrastructure requirements and operational cost. Journal of Pathology Informatics. 14. 100318–100318. 11 indexed citations

11.

Hanna, Matthew G., et al.. (2022). Current applications and challenges of artificial intelligence in pathology. SHILAP Revista de lepidopterología. 27. 300596–300596. 8 indexed citations

12.

Zhong, Elaine, Fresia Pareja, Matthew G. Hanna, et al.. (2022). Expression of novel neuroendocrine markers in breast carcinomas: a study of INSM1, ASCL1, and POU2F3. Human Pathology. 127. 102–111. 8 indexed citations

13.

Hanna, Matthew G., Orly Ardon, Victor E. Reuter, et al.. (2021). Integrating digital pathology into clinical practice. Modern Pathology. 35(2). 152–164. 83 indexed citations

14.

D’Alfonso, Timothy M., David Joon Ho, Matthew G. Hanna, et al.. (2021). Multi-magnification-based machine learning as an ancillary tool for the pathologic assessment of shaved margins for breast carcinoma lumpectomy specimens. Modern Pathology. 34(8). 1487–1494. 16 indexed citations

15.

Perron, Marjorie, Hannah Y. Wen, Matthew G. Hanna, Edi Brogi, & Dara S. Ross. (2020). HER2 Immunohistochemistry in Invasive Micropapillary Breast Carcinoma: Complete Assessment of an Incomplete Pattern. Archives of Pathology & Laboratory Medicine. 145(8). 979–987. 8 indexed citations

16.

Thomas, Roby, Ellis Levine, Adam Brufsky, et al.. (2020). Outcome of Everolimus-Based Therapy in Hormone-Receptor-Positive Metastatic Breast Cancer Patients After Progression on Palbociclib. Breast Cancer Basic and Clinical Research. 14. 2222502368–2222502368. 27 indexed citations

17.

Hanna, Matthew G., et al.. (2018). Augmented Reality Technology Using Microsoft HoloLens in Anatomic Pathology. Archives of Pathology & Laboratory Medicine. 142(5). 638–644. 153 indexed citations

18.

Garcia, Christine, et al.. (2017). Sharing Cellavision Blood Smear Images with Clinicians Via the Electronic Medical Record. Blood. 130. 5586–5586. 2 indexed citations

19.

Hanna, Matthew G., Shabnam Jaffer, Ira J. Bleiweiss, & Anupma Nayak. (2013). Minimally invasive mammary Paget’s disease without an underlying breast carcinoma. Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin. 463(3). 471–473. 6 indexed citations

20.

McKenna, Aaron, Matthew G. Hanna, Eric Banks, et al.. (2010). The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Research. 20(9). 1297–1303. 17384 indexed citations breakdown →

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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