David Coz
Impact in
- Health Informatics top 2%
- Artificial Intelligence in Healthcare and Education
- Ophthalmology top 5%
- Retinal Diseases and Treatments
- Retinal and Optic Conditions
Papers in
- Oncology 3
- Cutaneous Melanoma Detection and Management 3
-
- Machine Learning and Data Classification 1
- AI in cancer detection 1
- Explainable Artificial Intelligence (XAI) 1
- Co-authors
- Shawn Xu (2 shared papers)Arunachalam Narayanaswamy (2 shared papers)Katy Blumer (2 shared papers)Arjun B. Sood (1 shared paper)Greg S. Corrado (2 shared papers)Scott M. Barb (1 shared paper)Ankur Taly (2 shared papers)Lily Peng (3 shared papers)
- Journals
- Investigative Ophthalmology & Visual Science (1 paper)JAMA Network Open (1 paper)Ophthalmology (1 paper)Iproceedings (1 paper)
- Partner nations
- United States
In The Last Decade
David Coz
5 papers receiving 420 citations
David Coz's Hit Papers
Peers
Comparison fields: 5 of 83
- Health Informatics 82
- Ophthalmology 95
- Health Information Management 41
- Radiology, Nuclear Medicine and Imaging 185
- Artificial Intelligence 130
Countries citing papers authored by David Coz
This map shows the geographic impact of David Coz'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 David Coz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Coz more than expected).
Fields of papers citing papers by David Coz
This network shows the impact of papers produced by David Coz. 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 David Coz. The network helps show where David Coz may publish in the future.
Co-authors
The 25 scholars most cited alongside David Coz, 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 | Using a Deep Learning Algorithm and Integrated Gradients Explanation to Assist Grading for Diabetic Retinopathy Hit paper breakdown → | 2018 | 302 |
| 2 | 2021 | 112 | |
| 3 | DermGAN: Synthetic Generation of Clinical Skin Images with Pathology | 2019 | 14 |
| 4 | 2020 | 9 | |
| 5 | Assisted reads for diabetic retinopathy using a deep learning algorithm and integrated gradient explanation | 2018 | 2 |
| 6 | 2022 | 0 |
About David Coz
David Coz is a scholar working on Oncology, Artificial Intelligence, Health Information Management, Biophysics and Statistics, Probability and Uncertainty, having authored 6 papers that have together received 439 indexed citations. Recurring topics across this work include Cutaneous Melanoma Detection and Management (3 papers), Machine Learning and Data Classification (1 paper), Cell Image Analysis Techniques (1 paper), Dermatological diseases and infestations (1 paper), Artificial Intelligence in Healthcare (1 paper), Dermatology and Skin Diseases (1 paper), AI in cancer detection (1 paper) and Explainable Artificial Intelligence (XAI) (1 paper). The work is most often cited by research in Health Informatics (82 citations), Ophthalmology (95 citations), Health Information Management (41 citations), Radiology, Nuclear Medicine and Imaging (185 citations) and Artificial Intelligence (130 citations). David Coz has collaborated with scholars based in United States. Frequent co-authors include Shawn Xu, Arunachalam Narayanaswamy, Katy Blumer, Arjun B. Sood, Greg S. Corrado, Scott M. Barb, Ankur Taly, Lily Peng, Anthony Joseph and Zahra Rastegar. Their work appears in journals such as Investigative Ophthalmology & Visual Science, JAMA Network Open, Ophthalmology and Iproceedings.
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.