Jonathan J. James

2.6k total citations
67 papers, 1.8k citations indexed

About

Jonathan J. James is a scholar working on Oncology, Pulmonary and Respiratory Medicine and Artificial Intelligence. According to data from OpenAlex, Jonathan J. James has authored 67 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Oncology, 25 papers in Pulmonary and Respiratory Medicine and 24 papers in Artificial Intelligence. Recurrent topics in Jonathan J. James's work include AI in cancer detection (24 papers), Breast Lesions and Carcinomas (22 papers) and Global Cancer Incidence and Screening (17 papers). Jonathan J. James is often cited by papers focused on AI in cancer detection (24 papers), Breast Lesions and Carcinomas (22 papers) and Global Cancer Incidence and Screening (17 papers). Jonathan J. James collaborates with scholars based in United Kingdom, United States and Netherlands. Jonathan J. James's co-authors include Sarah E. Pinder, Eleanor Cornford, H.C. Burrell, Andrew Evans, Stephen Chan, Kwok‐Leung Cheung, Ian O. Ellis, Eleanor Gutteridge, Andy Evans and J.F.R. Robertson and has published in prestigious journals such as New England Journal of Medicine, The Lancet and Nature Communications.

In The Last Decade

Jonathan J. James

62 papers receiving 1.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jonathan J. James United Kingdom 23 700 636 613 532 522 67 1.8k
Nisha Sharma United Kingdom 21 394 0.6× 196 0.3× 475 0.8× 600 1.1× 339 0.6× 134 1.4k
Stamatia Destounis United States 21 494 0.7× 490 0.8× 759 1.2× 250 0.5× 482 0.9× 78 1.6k
Başak E. Doğan United States 24 609 0.9× 336 0.5× 1.3k 2.2× 1.2k 2.2× 356 0.7× 115 2.8k
Haydee Ojeda‐Fournier United States 16 264 0.4× 255 0.4× 941 1.5× 342 0.6× 557 1.1× 68 1.5k
Miri Sklair‐Levy Israel 19 195 0.3× 286 0.4× 570 0.9× 299 0.6× 230 0.4× 67 1.4k
Jee Suk Chang South Korea 25 745 1.1× 552 0.9× 592 1.0× 593 1.1× 94 0.2× 172 2.0k
Joo Hee South Korea 29 415 0.6× 579 0.9× 1.5k 2.5× 708 1.3× 721 1.4× 138 2.5k
Alana A. Lewin United States 20 254 0.4× 253 0.4× 488 0.8× 337 0.6× 233 0.4× 58 1.2k
William R. Poller United States 14 801 1.1× 384 0.6× 419 0.7× 1.2k 2.3× 472 0.9× 22 2.0k
Soo Yeon Hahn South Korea 29 342 0.5× 275 0.4× 918 1.5× 242 0.5× 272 0.5× 99 2.5k

Countries citing papers authored by Jonathan J. James

Since Specialization
Citations

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

Fields of papers citing papers by Jonathan J. James

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jonathan J. James. 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 Jonathan J. James. The network helps show where Jonathan J. James may publish in the future.

Co-authorship network of co-authors of Jonathan J. James

This figure shows the co-authorship network connecting the top 25 collaborators of Jonathan J. James. A scholar is included among the top collaborators of Jonathan J. James 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 Jonathan J. James. Jonathan J. James 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.
James, Jonathan J., et al.. (2025). Keeping AI on Track: Regular monitoring of algorithmic updates in mammography. European Journal of Radiology. 187. 112100–112100.
2.
Gilbert, Fiona J., Nicholas Roy Payne, Sarah Vinnicombe, et al.. (2025). Comparison of supplemental breast cancer imaging techniques—interim results from the BRAID randomised controlled trial. The Lancet. 405(10493). 1935–1944. 9 indexed citations
3.
Giannotti, Elisabetta, Thiemo J. A. van Nijnatten, Giulia Bicchierai, et al.. (2024). The role of contrast-enhanced mammography in the preoperative evaluation of invasive lobular carcinoma of the breast. Clinical Radiology. 79(6). e799–e806.
4.
Darker, Iain, Jonathan J. James, Keshthra Satchithananda, et al.. (2024). How long does it take to read a mammogram? Investigating the reading time of digital breast tomosynthesis and digital mammography. European Journal of Radiology. 177. 111535–111535. 5 indexed citations
5.
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Ng, Annie, Ben Glocker, Cary Oberije, et al.. (2023). Artificial Intelligence as Supporting Reader in Breast Screening: A Novel Workflow to Preserve Quality and Reduce Workload. Journal of Breast Imaging. 5(3). 267–276. 17 indexed citations
7.
Phillips, Peter, Jonathan J. James, Keshthra Satchithananda, et al.. (2023). Take a break: should breaks be enforced during digital breast tomosynthesis reading sessions?. European Radiology. 34(2). 1388–1398. 2 indexed citations
8.
Sharma, Nisha, A.Y. Ng, Jonathan J. James, et al.. (2023). Multi-vendor evaluation of artificial intelligence as an independent reader for double reading in breast cancer screening on 275,900 mammograms. BMC Cancer. 23(1). 460–460. 37 indexed citations
9.
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11.
James, Jonathan J., et al.. (2018). Contrast-enhanced spectral mammography (CESM). Clinical Radiology. 73(8). 715–723. 67 indexed citations
12.
Dey, Paola, et al.. (2016). A questionnaire to identify patellofemoral pain in the community: an exploration of measurement properties. BMC Musculoskeletal Disorders. 17(1). 237–237. 49 indexed citations
13.
James, Jonathan J., et al.. (2016). Contrast-enhanced spectral mammography improves diagnostic accuracy in the symptomatic setting. Clinical Radiology. 71(11). 1148–1155. 66 indexed citations
14.
James, Jonathan J., et al.. (2016). Measuring performance in the interpretation of chest radiographs: a pilot study. Clinical Radiology. 72(3). 230–235. 4 indexed citations
15.
Cornford, Eleanor, Anne Turnbull, Jonathan J. James, et al.. (2015). Accuracy of GE digital breast tomosynthesisvssupplementary mammographic views for diagnosis of screen-detected soft-tissue breast lesions. British Journal of Radiology. 89(1058). 20150735–20150735. 9 indexed citations
16.
Mhlanga, Joyce, Sarah Doyle, Jonathan J. James, et al.. (2011). The prognostic significance of computerised tomography findings in women with liver metastases from breast cancer. The Breast. 20(5). 455–459. 2 indexed citations
17.
Gilbert, Fiona J., Susan Astley, Maureen Gc Gillan, et al.. (2008). Single Reading with Computer-Aided Detection for Screening Mammography. New England Journal of Medicine. 359(16). 1675–1684. 195 indexed citations
18.
Cornford, Eleanor, et al.. (2005). The pathological and radiological features of screen-detected breast cancers diagnosed following arbitration of discordant double reading opinions. Clinical Radiology. 60(11). 1182–1187. 22 indexed citations
19.
Evans, Andy, Jonathan J. James, Stephen Chan, et al.. (2004). Brain metastases from breast cancer: identification of a high-risk group. Clinical Oncology. 16(5). 345–349. 128 indexed citations
20.
Wyld, Lynda, Eleanor Gutteridge, Sarah E. Pinder, et al.. (2003). Prognostic factors for patients with hepatic metastases from breast cancer. British Journal of Cancer. 89(2). 284–290. 153 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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