Matthew B. A. McDermott
- Artificial Intelligence top 1%
- Health Informatics top 0.1%
- Molecular Biology
- Radiology, Nuclear Medicine and Imaging top 5%
- Health Information Management top 1%
- Co-authors
- Marzyeh GhassemiTristan NaumannWei‐Hung WengWilliam BoagEmily AlsentzerJohn R. MurphyHaoran ZhangIrene Y. Chen
- Topics
- Machine Learning in Healthcare (8 papers)Topic Modeling (5 papers)Artificial Intelligence in Healthcare and Education (5 papers)
- Partner nations
- United StatesCanadaUnited Kingdom
In The Last Decade
Matthew B. A. McDermott
22 papers receiving 1.5k citations
Hit Papers
Peers
Comparison fields: 5 of 138
- Artificial Intelligence 1.0k
- Health Informatics 421
- Molecular Biology 327
- Radiology, Nuclear Medicine and Imaging 316
- Health Information Management 154
Countries citing papers authored by Matthew B. A. McDermott
This map shows the geographic impact of Matthew B. A. McDermott'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 Matthew B. A. McDermott with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthew B. A. McDermott more than expected).
Fields of papers citing papers by Matthew B. A. McDermott
This network shows the impact of papers produced by Matthew B. A. McDermott. 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 Matthew B. A. McDermott. The network helps show where Matthew B. A. McDermott may publish in the future.
Co-authorship network of co-authors of Matthew B. A. McDermott
This figure shows the co-authorship network connecting the top 25 collaborators of Matthew B. A. McDermott. A scholar is included among the top collaborators of Matthew B. A. McDermott 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 Matthew B. A. McDermott. Matthew B. A. McDermott is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 4 | |
| 3 | 2 | |
| 4 | 12 | |
| 5 | 51 | |
| 6 | 3 | |
| 7 | 6 | |
| 8 | Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populationsbreakdown → | 361 |
| 9 | 151 | |
| 10 | Reproducibility in Machine Learning for Health | 3 |
| 11 | Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks | 5 |
| 12 | Publicly Available Clinicalbreakdown → | 781 |
| 13 | Clinically Accurate Chest X-Ray Report Generation. | 16 |
| 14 | Baselines for Chest X-Ray Report Generation | 13 |
| 15 | 19 | |
| 16 | 42 | |
| 17 | 5 | |
| 18 | 13 | |
| 19 | 8 | |
| 20 | 1 |
About Matthew B. A. McDermott
Matthew B. A. McDermott is a scholar working on Health Informatics, Aging and Family Practice, having authored 23 papers that have together received 1.6k indexed citations. Recurring topics across this work include Machine Learning in Healthcare (8 papers), Topic Modeling (5 papers) and Artificial Intelligence in Healthcare and Education (5 papers). The work is most often cited by research in Health Informatics (421 citations), Health Information Management (154 citations) and Artificial Intelligence (1.0k citations). Matthew B. A. McDermott has collaborated with scholars based in United States, Canada and United Kingdom. Frequent co-authors include Marzyeh Ghassemi, Tristan Naumann, Wei‐Hung Weng, William Boag, Emily Alsentzer, John R. Murphy, Haoran Zhang, Irene Y. Chen, Laleh Seyyed-Kalantari and Shirly Wang. Their work appears in journals such as Nature Medicine, Science Translational Medicine and Molecular Biology of the Cell.
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.