John R. Zech

2.6k total citations · 1 hit paper
29 papers, 1.6k citations indexed

About

John R. Zech is a scholar working on Radiology, Nuclear Medicine and Imaging, Epidemiology and Health Informatics. According to data from OpenAlex, John R. Zech has authored 29 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Radiology, Nuclear Medicine and Imaging, 10 papers in Epidemiology and 9 papers in Health Informatics. Recurrent topics in John R. Zech's work include Artificial Intelligence in Healthcare and Education (9 papers), COVID-19 diagnosis using AI (8 papers) and Machine Learning in Healthcare (4 papers). John R. Zech is often cited by papers focused on Artificial Intelligence in Healthcare and Education (9 papers), COVID-19 diagnosis using AI (8 papers) and Machine Learning in Healthcare (4 papers). John R. Zech collaborates with scholars based in United States and Switzerland. John R. Zech's co-authors include Eric K. Oermann, Anthony Costa, J. Titano, Marcus A. Badgeley, Manway Liu, Samuel K. Cho, Jun Kim, Javin Schefflein, Joseph Lehár and Margaret Pain and has published in prestigious journals such as Nature Medicine, Bioinformatics and PLoS ONE.

In The Last Decade

John R. Zech

26 papers receiving 1.5k 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.

Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study

2018 826 citations John R. Zech, Marcus A. Badgeley et al. PLoS Medicine profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
John R. Zech United States	12	722	618	504	217	167	29	1.6k
Marcus A. Badgeley United States	16	708 1.0×	613 1.0×	431 0.9×	230 1.1×	223 1.3×	21	2.0k
Joseph R. Ledsam United Kingdom	8	725 1.0×	491 0.8×	496 1.0×	194 0.9×	142 0.9×	14	1.9k
Keno K. Bressem Germany	26	785 1.1×	427 0.7×	534 1.1×	229 1.1×	136 0.8×	109	1.8k
Luke Oakden‐Rayner Australia	13	490 0.7×	528 0.9×	551 1.1×	157 0.7×	173 1.0×	18	1.4k
J. Titano United States	14	746 1.0×	518 0.8×	394 0.8×	176 0.8×	191 1.1×	31	1.7k
Christoph Kern Germany	14	983 1.4×	449 0.7×	494 1.0×	158 0.7×	177 1.1×	36	1.8k
Gabriella Moraes United Kingdom	12	883 1.2×	481 0.8×	510 1.0×	158 0.7×	101 0.6×	22	1.6k
Oishi Banerjee United States	5	687 1.0×	773 1.3×	912 1.8×	201 0.9×	104 0.6×	7	2.2k
Bryan He United States	14	672 0.9×	348 0.6×	222 0.4×	178 0.8×	108 0.6×	31	1.6k
Anuj Pareek United States	12	634 0.9×	639 1.0×	320 0.6×	159 0.7×	76 0.5×	23	1.5k

Countries citing papers authored by John R. Zech

Since Specialization

Citations

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

Fields of papers citing papers by John R. Zech

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John R. Zech. 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 John R. Zech. The network helps show where John R. Zech may publish in the future.

Co-authorship network of co-authors of John R. Zech

This figure shows the co-authorship network connecting the top 25 collaborators of John R. Zech. A scholar is included among the top collaborators of John R. Zech 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 John R. Zech. John R. Zech 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

Wong, Tony T., et al.. (2025). AI as teacher: effectiveness of an AI-based training module to improve trainee pediatric fracture detection. Skeletal Radiology. 54(9). 1949–1957. 2 indexed citations

Zech, John R., et al.. (2025). Transformer-Based Open-Source Whisper Software Versus Leading Commercial Speech Recognition Software for Radiology Transcription. American Journal of Roentgenology. 225(2). e2532903–e2532903.

Zech, John R., et al.. (2024). Evaluating the Performance and Bias of Natural Language Processing Tools in Labeling Chest Radiograph Reports. Radiology. 313(1). e232746–e232746. 2 indexed citations

Patel, Aanand A., Rajat Kalra, Avanti Adhia, et al.. (2024). Fully Automated Measurement of the Insall-Salvati Ratio with Artificial Intelligence. Journal of Imaging Informatics in Medicine. 37(2). 601–610. 2 indexed citations

Zech, John R., et al.. (2024). Artificial intelligence improves resident detection of pediatric and young adult upper extremity fractures. Skeletal Radiology. 53(12). 2643–2651. 16 indexed citations

Sehgal, Priya, John R. Zech, Yael R. Nobel, et al.. (2023). Visceral Adiposity Independently Predicts Time to Flare in Inflammatory Bowel Disease but Body Mass Index Does Not. Inflammatory Bowel Diseases. 30(4). 594–601. 23 indexed citations

Zech, John R., et al.. (2023). Detecting pediatric wrist fractures using deep-learning-based object detection. Pediatric Radiology. 53(6). 1125–1134. 31 indexed citations

Zech, John R., Diego Jaramillo, Jaan Altosaar, Charles A. Popkin, & Tony T. Wong. (2023). Artificial intelligence to identify fractures on pediatric and young adult upper extremity radiographs. Pediatric Radiology. 53(12). 2386–2397. 24 indexed citations

Yi, Paul H., Hillary W. Garner, Anna Hirschmann, et al.. (2023). Clinical Applications, Challenges, and Recommendations for Artificial Intelligence in Musculoskeletal and Soft-Tissue Ultrasound: AJR Expert Panel Narrative Review. American Journal of Roentgenology. 222(3). e2329530–e2329530. 7 indexed citations

10.

Sehgal, Priya, et al.. (2023). P835 Visceral adiposity independently predicts time to flare in inflammatory bowel disease, but BMI does not. Journal of Crohn s and Colitis. 17(Supplement_1). i964–i965. 1 indexed citations

11.

Zech, John R., et al.. (2022). Inferring pediatric knee skeletal maturity from MRI using deep learning. Skeletal Radiology. 51(8). 1671–1677. 2 indexed citations

12.

Tummalapalli, Sri Lekha, John R. Zech, Hyung J. Cho, & Celine Goetz. (2021). Risk stratification for hydronephrosis in the evaluation of acute kidney injury: a cross-sectional analysis. BMJ Open. 11(8). e046761–e046761. 1 indexed citations

13.

Zech, John R., et al.. (2020). Identifying Factors Important to Patients for Resuming Elective Imaging During the COVID-19 Pandemic. Journal of the American College of Radiology. 18(4). 590–600. 4 indexed citations

14.

Kaji, Deepak, John R. Zech, Jun Kim, et al.. (2019). An attention based deep learning model of clinical events in the intensive care unit. PLoS ONE. 14(2). e0211057–e0211057. 133 indexed citations

15.

Zech, John R., Jessica Zosa Forde, J. Titano, et al.. (2019). Detecting insertion, substitution, and deletion errors in radiology reports using neural sequence-to-sequence models. Annals of Translational Medicine. 7(11). 233–233. 11 indexed citations

16.

Titano, J., D. Biederman, John R. Zech, et al.. (2018). Safety and Outcomes of Transradial Access in Patients with International Normalized Ratio 1.5 or above. Journal of Vascular and Interventional Radiology. 29(3). 383–388. 8 indexed citations

17.

Zech, John R., Marcus A. Badgeley, Manway Liu, et al.. (2018). Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study. PLoS Medicine. 15(11). e1002683–e1002683. 826 indexed citations breakdown →

18.

Badgeley, Marcus A., Manway Liu, Benjamin S. Glicksberg, et al.. (2018). CANDI: an R package and Shiny app for annotating radiographs and evaluating computer-aided diagnosis. Bioinformatics. 35(9). 1610–1612. 4 indexed citations

19.

Titano, J., Marcus A. Badgeley, Javin Schefflein, et al.. (2018). Automated deep-neural-network surveillance of cranial images for acute neurologic events. Nature Medicine. 24(9). 1337–1341. 270 indexed citations

20.

Zech, John R., Gregg Husk, Thomas A. Moore, & Jason S. Shapiro. (2016). Measuring the Degree of Unmatched Patient Records in a Health Information Exchange Using Exact Matching. Applied Clinical Informatics. 7(2). 330–340. 7 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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