Guy Handelman

1.4k total citations · 1 hit paper
5 papers, 914 citations indexed

About

Guy Handelman is a scholar working on Pathology and Forensic Medicine, Health and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, Guy Handelman has authored 5 papers receiving a total of 914 indexed citations (citations by other indexed papers that have themselves been cited), including 1 paper in Pathology and Forensic Medicine, 1 paper in Health and 1 paper in Cardiology and Cardiovascular Medicine. Recurrent topics in Guy Handelman's work include Radiomics and Machine Learning in Medical Imaging (1 paper), Artificial Intelligence in Healthcare (1 paper) and Machine Learning in Healthcare (1 paper). Guy Handelman is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (1 paper), Artificial Intelligence in Healthcare (1 paper) and Machine Learning in Healthcare (1 paper). Guy Handelman collaborates with scholars based in Ireland, Australia and United Kingdom. Guy Handelman's co-authors include Ronil V. Chandra, Hong Kuan Kok, Amir H. Razavi, Hamed Asadi, Michael J. Lee, Shiwei Huang, Hamed Asadi, Mark Brooks, Vincent Thijs and Christen Barras and has published in prestigious journals such as American Journal of Roentgenology, Journal of Internal Medicine and Acta Neurologica Scandinavica.

In The Last Decade

Guy Handelman

5 papers receiving 891 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.

eDoctor: machine learning and the future of medicine

2018 592 citations Guy Handelman, Hong Kuan Kok et al. Journal of Internal Medicine profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Guy Handelman Ireland	5	189	163	158	128	124	5	914
Amir H. Razavi Canada	7	183 1.0×	191 1.2×	124 0.8×	118 0.9×	119 1.0×	15	937
Ing Wei Khor Singapore	6	207 1.1×	211 1.3×	92 0.6×	126 1.0×	87 0.7×	7	935
David E. Liston United States	6	146 0.8×	322 2.0×	97 0.6×	148 1.2×	151 1.2×	9	1.3k
Michael J. Eisses United States	8	149 0.8×	322 2.0×	133 0.8×	212 1.7×	201 1.6×	11	1.4k
Sunil Gupta Australia	14	168 0.9×	428 2.6×	64 0.4×	161 1.3×	181 1.5×	43	1.3k
Wenqi Shi United States	15	340 1.8×	225 1.4×	80 0.5×	109 0.9×	60 0.5×	79	954
Oğuz Akbilgiç United States	22	107 0.6×	202 1.2×	115 0.7×	138 1.1×	148 1.2×	81	1.3k
Julián Acosta United States	14	245 1.3×	234 1.4×	82 0.5×	243 1.9×	41 0.3×	45	1.2k
Hendrikus J. A. van Os Netherlands	12	120 0.6×	127 0.8×	170 1.1×	307 2.4×	59 0.5×	31	806
Rohit Ghosh United States	6	284 1.5×	216 1.3×	70 0.4×	183 1.4×	95 0.8×	8	886

Countries citing papers authored by Guy Handelman

Since Specialization

Citations

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

Fields of papers citing papers by Guy Handelman

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Guy Handelman. 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 Guy Handelman. The network helps show where Guy Handelman may publish in the future.

Co-authorship network of co-authors of Guy Handelman

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

All Works

Sort: Min cites: Since: Top N: Style:

5 of 5 papers shown

1.

Maingard, Julian, Christen Barras, Hong Kuan Kok, et al.. (2019). Carotid artery stenting: Current state of evidence and future directions. Acta Neurologica Scandinavica. 139(4). 318–333. 38 indexed citations

2.

Handelman, Guy, et al.. (2018). eDoctor: machine learning and the future of medicine. Journal of Internal Medicine. 284(6). 603–619. 592 indexed citations breakdown →

3.

Handelman, Guy, et al.. (2018). Media messaging in diagnosis of acute CXR pathology: an interobserver study among residents. Internal and Emergency Medicine. 13(8). 1257–1263. 6 indexed citations

4.

Handelman, Guy, Hong Kuan Kok, Ronil V. Chandra, et al.. (2018). Peering Into the Black Box of Artificial Intelligence: Evaluation Metrics of Machine Learning Methods. American Journal of Roentgenology. 212(1). 38–43. 269 indexed citations

5.

Rogers, Ailín C., et al.. (2017). Meta-analysis of the clinicopathological characteristics and peri-operative outcomes of colorectal cancer in obese patients. Cancer Epidemiology. 51. 23–29. 9 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.

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