Michaela Hardt
Impact in
- Health Informatics top 0.5%
- Artificial Intelligence in Healthcare and Education
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- Artificial Intelligence in Healthcare
Papers in
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- Privacy-Preserving Technologies in Data 2
- Cryptography and Data Security 1
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- Healthcare cost, quality, practices 1
- Co-authors
- Alvin Rajkomar (2 shared papers)Greg S. Corrado (1 shared paper)Marshall H. Chin (1 shared paper)Michael Howell (1 shared paper)Suman Nath (1 shared paper)Johannes Gehrke (1 shared paper)Paul Francis (1 shared paper)İstemi Ekin Akkuş (1 shared paper)
- Journals
- npj Digital Medicine (1 paper)Annals of Internal Medicine (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)Max Planck Digital Library (1 paper)
- Partner nations
- United StatesGermany
In The Last Decade
Michaela Hardt
5 papers receiving 691 citations
Michaela Hardt's Hit Papers
Peers
Comparison fields: 5 of 97
- Health Informatics 247
- Health Information Management 62
- Artificial Intelligence 229
- Safety Research 52
- Family Practice 10
Countries citing papers authored by Michaela Hardt
This map shows the geographic impact of Michaela Hardt'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 Michaela Hardt with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michaela Hardt more than expected).
Fields of papers citing papers by Michaela Hardt
This network shows the impact of papers produced by Michaela Hardt. 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 Michaela Hardt. The network helps show where Michaela Hardt may publish in the future.
Co-authors
The 15 scholars most cited alongside Michaela Hardt, 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 | Ensuring Fairness in Machine Learning to Advance Health Equity Hit paper breakdown → | 2018 | 603 |
| 2 | 2012 | 59 | |
| 3 | 2012 | 33 | |
| 4 | 2018 | 18 | |
| 5 | 2025 | 1 |
About Michaela Hardt
Michaela Hardt is a scholar working on Artificial Intelligence, General Health Professions, Health Information Management, Sociology and Political Science and Information Systems, having authored 5 papers that have together received 714 indexed citations. Recurring topics across this work include Privacy-Preserving Technologies in Data (2 papers), Artificial Intelligence in Healthcare and Education (1 paper), Artificial Intelligence in Healthcare (1 paper), Healthcare cost, quality, practices (1 paper), Mobile Crowdsensing and Crowdsourcing (1 paper), Ethics in Clinical Research (1 paper), Cryptography and Data Security (1 paper) and Time Series Analysis and Forecasting (1 paper). The work is most often cited by research in Health Informatics (247 citations), Health Information Management (62 citations), Artificial Intelligence (229 citations), Safety Research (52 citations) and Family Practice (10 citations). Michaela Hardt has collaborated with scholars based in United States and Germany. Frequent co-authors include Alvin Rajkomar, Greg S. Corrado, Marshall H. Chin, Michael Howell, Suman Nath, Johannes Gehrke, Paul Francis, İstemi Ekin Akkuş, Ruichuan Chen and Kathryn Rough. Their work appears in journals such as npj Digital Medicine, Annals of Internal Medicine, Proceedings of the AAAI Conference on Artificial Intelligence and Max Planck Digital Library.
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.