Samuel G. Finlayson
- Artificial Intelligence top 5%
- Radiology, Nuclear Medicine and Imaging top 5%
- Health Informatics top 0.5%
- Molecular Biology
- Public Health, Environmental and Occupational Health
- Co-authors
- Isaac S. KohaneAndrew L. BeamJonathan ZittrainJoichi ItoNigam H. ShahPaea LePenduBrett K. Beaulieu‐JonesWilliam Yuan
- Topics
- Artificial Intelligence in Healthcare and Education (7 papers)Machine Learning in Healthcare (5 papers)AI in cancer detection (3 papers)
- Journals
- ScienceNature CommunicationsPLoS ONE
- Partner nations
- United StatesUnited KingdomNigeria
In The Last Decade
Samuel G. Finlayson
20 papers receiving 1.1k citations
Hit Papers
Peers
Comparison fields: 5 of 133
- Artificial Intelligence 470
- Radiology, Nuclear Medicine and Imaging 262
- Health Informatics 212
- Molecular Biology 189
- Public Health, Environmental and Occupational Health 91
Countries citing papers authored by Samuel G. Finlayson
This map shows the geographic impact of Samuel G. Finlayson'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 Samuel G. Finlayson with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Samuel G. Finlayson more than expected).
Fields of papers citing papers by Samuel G. Finlayson
This network shows the impact of papers produced by Samuel G. Finlayson. 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 Samuel G. Finlayson. The network helps show where Samuel G. Finlayson may publish in the future.
Co-authorship network of co-authors of Samuel G. Finlayson
This figure shows the co-authorship network connecting the top 25 collaborators of Samuel G. Finlayson. A scholar is included among the top collaborators of Samuel G. Finlayson 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 Samuel G. Finlayson. Samuel G. Finlayson is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 7 | |
| 2 | 0 | |
| 3 | 39 | |
| 4 | 4 | |
| 5 | 13 | |
| 6 | Precision Medicine: Using Artificial Intelligence to Improve Diagnostics and Healthcare. | 3 |
| 7 | 115 | |
| 8 | 3 | |
| 9 | Subgraph Neural Networks | 5 |
| 10 | 53 | |
| 11 | Adversarial attacks on medical machine learningbreakdown → | 525 |
| 12 | 105 | |
| 13 | 42 | |
| 14 | 17 | |
| 15 | Towards generative adversarial networks as a new paradigm for radiology education | 1 |
| 16 | 34 | |
| 17 | 30 | |
| 18 | 15 | |
| 19 | 61 | |
| 20 | 1 |
About Samuel G. Finlayson
Samuel G. Finlayson is a scholar working on Health Informatics, Health Information Management and Artificial Intelligence, having authored 22 papers that have together received 1.1k indexed citations. Recurring topics across this work include Artificial Intelligence in Healthcare and Education (7 papers), Machine Learning in Healthcare (5 papers) and AI in cancer detection (3 papers). The work is most often cited by research in Health Informatics (212 citations), Artificial Intelligence (470 citations) and Health Information Management (60 citations). Samuel G. Finlayson has collaborated with scholars based in United States, United Kingdom and Nigeria. Frequent co-authors include Isaac S. Kohane, Andrew L. Beam, Jonathan Zittrain, Joichi Ito, Nigam H. Shah, Paea LePendu, Brett K. Beaulieu‐Jones, William Yuan, Russ B. Altman and Vinay Prasad. Their work appears in journals such as Science, Nature Communications and PLoS ONE.
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.