John Maddison

876 total citations · 1 hit paper
18 papers, 542 citations indexed

About

John Maddison is a scholar working on Artificial Intelligence, Ophthalmology and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, John Maddison has authored 18 papers receiving a total of 542 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Artificial Intelligence, 3 papers in Ophthalmology and 3 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in John Maddison's work include Retinal Diseases and Treatments (3 papers), Retinal Imaging and Analysis (3 papers) and Machine Learning in Healthcare (2 papers). John Maddison is often cited by papers focused on Retinal Diseases and Treatments (3 papers), Retinal Imaging and Analysis (3 papers) and Machine Learning in Healthcare (2 papers). John Maddison collaborates with scholars based in United Kingdom, Australia and United States. John Maddison's co-authors include Knut Liestøl, Arild Nesbakken, Neil A. Shepherd, Rachel Kerr, Tarjei S. Hveem, John Arne Nesheim, Fritz Albregtsen, David J. Kerr, Inger Nina Farstad and Ian Tomlinson and has published in prestigious journals such as The Lancet, Progress in Retinal and Eye Research and Journal of Clinical Pathology.

In The Last Decade

John Maddison

16 papers receiving 535 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.

Deep learning for prediction of colorectal cancer outcome: a discovery and validation study

2020 393 citations Ole-Johan Skrede, Sepp de Raedt et al. The Lancet profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
John Maddison United Kingdom	8	295	215	189	72	66	18	542
Benoît Schmauch France	8	448 1.5×	370 1.7×	126 0.7×	134 1.9×	111 1.7×	14	763
Meriem Sefta France	5	305 1.0×	347 1.6×	157 0.8×	155 2.2×	143 2.2×	6	688
Xiao Hu China	12	253 0.9×	127 0.6×	308 1.6×	37 0.5×	57 0.9×	40	631
Arjun B. Sood United States	10	193 0.7×	81 0.4×	95 0.5×	29 0.4×	56 0.8×	15	464
Jefree J. Schulte United States	12	126 0.4×	151 0.7×	148 0.8×	73 1.0×	80 1.2×	37	741
Ole-Johan Skrede United Kingdom	3	401 1.4×	319 1.5×	241 1.3×	110 1.5×	67 1.0×	4	654
John Arne Nesheim Norway	5	266 0.9×	204 0.9×	217 1.1×	82 1.1×	49 0.7×	9	477
Lily H. Peng United States	6	376 1.3×	446 2.1×	93 0.5×	86 1.2×	77 1.2×	6	689
Justinas Besusparis Lithuania	10	115 0.4×	146 0.7×	114 0.6×	112 1.6×	63 1.0×	29	369

Countries citing papers authored by John Maddison

Since Specialization

Citations

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

Fields of papers citing papers by John Maddison

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of John Maddison

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

18 of 18 papers shown

Cook, Benjamin K., Brandon Stretton, John Maddison, et al.. (2025). Use of Large Language Models for Rapid Quantitative Feedback in Case-Based Learning: A Pilot Study. Medical Science Educator. 35(3). 1169–1171. 3 indexed citations

Kovoor, Joshua G., Brandon Stretton, Aashray Gupta, et al.. (2025). The Adelaide Score: prospective implementation of an artificial intelligence system to improve hospital and cost efficiency. ANZ Journal of Surgery. 95(3). 342–349.

Stretton, Brandon, Joshua G. Kovoor, Aashray Gupta, et al.. (2024). Impact of frailty, malnutrition and socioeconomic status on perioperative outcomes. Age and Ageing. 53(12). 4 indexed citations

Cook, Benjamin K., Brandon Stretton, Joshua G. Kovoor, et al.. (2024). A brief history of ramping. Internal Medicine Journal. 54(9). 1577–1580. 1 indexed citations

Cook, Benjamin K., Brandon Stretton, Joshua G. Kovoor, et al.. (2024). Large language models can effectively extract stroke and reperfusion audit data from medical free-text discharge summaries. Journal of Clinical Neuroscience. 129. 110847–110847. 6 indexed citations

Teo, Melissa, Brandon Stretton, Joshua G. Kovoor, et al.. (2024). Medication shortage behaviour change with multidisciplinary clinician-designed digital notification intervention. International Journal of Pharmacy Practice. 33(1). 124–126.

Kovoor, Joshua G., Brandon Stretton, Aashray Gupta, et al.. (2024). Copying in medical documentation: developing an evidence‐based approach. Internal Medicine Journal. 55(1). 84–88. 2 indexed citations

Cordeiro, M. Francesca, et al.. (2021). Detecting retinal cell stress and apoptosis with DARC: Progression from lab to clinic. Progress in Retinal and Eye Research. 86. 100976–100976. 40 indexed citations

Skrede, Ole-Johan, Sepp de Raedt, Andreas Kleppe, et al.. (2020). Deep learning for prediction of colorectal cancer outcome: a discovery and validation study. The Lancet. 395(10221). 350–360. 393 indexed citations breakdown →

10.

Normando, Eduardo, Timothy E. Yap, John Maddison, et al.. (2020). A CNN-aided method to predict glaucoma progression using DARC (Detection of Apoptosing Retinal Cells). Expert Review of Molecular Diagnostics. 20(7). 737–748. 32 indexed citations

11.

Corazza, Paolo, John Maddison, Li Guo, et al.. (2020). Predicting wet age-related macular degeneration (AMD) using DARC (detecting apoptosing retinal cells) AI (artificial intelligence) technology. Expert Review of Molecular Diagnostics. 21(1). 109–118. 8 indexed citations

12.

Blaker, Yngvild Nuvin, John Maddison, Tarjei S. Hveem, et al.. (2014). Computerized image analysis of the Ki‐67 proliferation index in mantle cell lymphoma. Histopathology. 67(1). 62–69. 11 indexed citations

13.

Paish, Emma C., Andrew R. Green, Emad A. Rakha, et al.. (2009). Three-dimensional reconstruction of sentinel lymph nodes with metastatic breast cancer indicates three distinct patterns of tumour growth. Journal of Clinical Pathology. 62(7). 617–623. 14 indexed citations

14.

Maddison, John, Emma C. Paish, Thomas Kurien, et al.. (2006). 3D Reconstruction and Visualisation for Exploration of Human Axillary Lymph Nodes. 55–59. 1 indexed citations

15.

Kurien, Thomas, Emma C. Paish, J Ronan, et al.. (2005). Three dimensional reconstruction of a human breast carcinoma using routine laboratory equipment and immunohistochemistry. Journal of Clinical Pathology. 58(9). 968–972. 13 indexed citations

16.

Daly, Craig, Elisabet Vila, Ana M. Briones, et al.. (2002). Analysing the 3D Structure of Blood Vessels using Confocal Microscopy. ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam). 12 indexed citations

17.

Tyrer, N. M., John Maddison, David Shepherd, & Darren W. Williams. (2000). Confocal quality imaging of afferent neurons from semi-thin sections of Drosophila ganglia. Neuroscience Letters. 296(2-3). 93–96. 1 indexed citations

18.

Daly, Craig, E. Vila, Silvia M. Arribas, et al.. (2000). 3-dimensional analysis of vascular structure, function & receptor distribution using confocal laser scanning microscopy. 1 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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