John Maddison
Impact in
- Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education
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- Radiomics and Machine Learning in Medical Imaging
- Retinal Imaging and Analysis
Papers in
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- Retinal Imaging and Analysis 3
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- Machine Learning in Healthcare 1
- Co-authors
- Knut Liestøl (2 shared papers)John Arne Nesheim (2 shared papers)Håvard E. Danielsen (2 shared papers)Tarjei S. Hveem (2 shared papers)Enric Domingo (1 shared paper)Ole-Johan Skrede (1 shared paper)Andreas Kleppe (1 shared paper)Arild Nesbakken (1 shared paper)
- Journals
- Journal of Clinical Pathology (2 papers)Expert Review of Molecular Diagnostics (2 papers)Histopathology (1 paper)Age and Ageing (1 paper)The Lancet (1 paper)
- Partner nations
- United KingdomAustraliaUnited States
In The Last Decade
John Maddison
17 papers receiving 580 citations
John Maddison's Hit Papers
Peers
Comparison fields: 5 of 87
- Health Informatics 38
- Radiology, Nuclear Medicine and Imaging 226
- Ophthalmology 56
- Artificial Intelligence 210
- Oncology 137
Countries citing papers authored by John Maddison
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
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-authors
The 25 scholars most cited alongside John Maddison, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Deep learning for prediction of colorectal cancer outcome: a discovery and validation study Hit paper breakdown → | 2020 | 429 |
| 2 | 2021 | 41 | |
| 3 | 2020 | 34 | |
| 4 | 2009 | 14 | |
| 5 | 2005 | 13 | |
| 6 | Analysing the 3D Structure of Blood Vessels using Confocal Microscopy | 2002 | 13 |
| 7 | 2014 | 11 | |
| 8 | 2024 | 10 | |
| 9 | 2020 | 8 | |
| 10 | 2024 | 5 | |
| 11 | 2025 | 4 | |
| 12 | 2024 | 3 | |
| 13 | 2006 | 1 | |
| 14 | 2024 | 1 | |
| 15 | 2000 | 1 | |
| 16 | 2025 | 1 | |
| 17 | 3-dimensional analysis of vascular structure, function & receptor distribution using confocal laser scanning microscopy | 2000 | 1 |
| 18 | 2024 | 0 |
About John Maddison
John Maddison is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Molecular Biology, Pathology and Forensic Medicine and Cellular and Molecular Neuroscience, having authored 18 papers that have together received 590 indexed citations. Recurring topics across this work include Retinal Imaging and Analysis (3 papers), Optical Coherence Tomography Applications (1 paper), Retinal Diseases and Treatments (1 paper), Advanced Vision and Imaging (1 paper), Lymphoma Diagnosis and Treatment (1 paper), Primary Care and Health Outcomes (1 paper), Neurobiology and Insect Physiology Research (1 paper) and Machine Learning in Healthcare (1 paper). The work is most often cited by research in Health Informatics (38 citations), Radiology, Nuclear Medicine and Imaging (226 citations), Ophthalmology (56 citations), Artificial Intelligence (210 citations) and Oncology (137 citations). John Maddison has collaborated with scholars based in United Kingdom, Australia and United States. Frequent co-authors include Knut Liestøl, John Arne Nesheim, Håvard E. Danielsen, Tarjei S. Hveem, Enric Domingo, Ole-Johan Skrede, Andreas Kleppe, Arild Nesbakken, Sepp de Raedt and Marco Novelli. Their work appears in journals such as Journal of Clinical Pathology, Expert Review of Molecular Diagnostics, Histopathology, Age and Ageing and The Lancet.
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.