Amin Dada
Impact in
- Health Informatics top 1%
- Artificial Intelligence in Healthcare and Education
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- Clinical Reasoning and Diagnostic Skills
Papers in
-
- Topic Modeling 4
- Machine Learning in Healthcare 4
- Natural Language Processing Techniques 2
- Semantic Web and Ontologies 1
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- Artificial Intelligence in Healthcare and Education 5
- Co-authors
- Jens Kleesiek (7 shared papers)Jan Egger (2 shared papers)Jianning Li (1 shared paper)Behrus Puladi (1 shared paper)Moon Kim (1 shared paper)Felix Busch (1 shared paper)Felix Nensa (4 shared papers)Daniel Truhn (2 shared papers)
- Journals
- Journal of the American Medical Informatics Association (2 papers)Computer Methods and Programs in Biomedicine (1 paper)European Radiology (1 paper)Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz (1 paper)Healthcare Analytics (1 paper)
- Partner nations
- GermanyCanadaUnited States
In The Last Decade
Amin Dada
5 papers receiving 184 citations
Hit Papers
Peers
Comparison fields: 5 of 74
- Health Informatics 108
- Family Practice 11
- Health Information Management 12
- Artificial Intelligence 81
- Issues, ethics and legal aspects 3
Countries citing papers authored by Amin Dada
This map shows the geographic impact of Amin Dada'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 Amin Dada with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Amin Dada more than expected).
Fields of papers citing papers by Amin Dada
This network shows the impact of papers produced by Amin Dada. 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 Amin Dada. The network helps show where Amin Dada may publish in the future.
Co-authors
The 20 scholars most cited alongside Amin Dada, 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 | ChatGPT in healthcare: A taxonomy and systematic review Hit paper breakdown → | 2024 | 171 |
| 2 | 2023 | 7 | |
| 3 | 2025 | 5 | |
| 4 | 2025 | 3 | |
| 5 | 2025 | 1 | |
| 6 | 2025 | 0 | |
| 7 | 2025 | 0 | |
| 8 | 2024 | 0 | |
| 9 | 2025 | 0 |
About Amin Dada
Amin Dada is a scholar working on Artificial Intelligence, Health Informatics, Health Information Management, Radiology, Nuclear Medicine and Imaging and Molecular Biology, having authored 9 papers that have together received 187 indexed citations. Recurring topics across this work include Artificial Intelligence in Healthcare and Education (5 papers), Topic Modeling (4 papers), Machine Learning in Healthcare (4 papers), Natural Language Processing Techniques (2 papers), Artificial Intelligence in Healthcare (2 papers), Semantic Web and Ontologies (1 paper), COVID-19 diagnosis using AI (1 paper) and Video Analysis and Summarization (1 paper). The work is most often cited by research in Health Informatics (108 citations), Family Practice (11 citations), Health Information Management (12 citations), Artificial Intelligence (81 citations) and Issues, ethics and legal aspects (3 citations). Amin Dada has collaborated with scholars based in Germany, Canada and United States. Frequent co-authors include Jens Kleesiek, Jan Egger, Jianning Li, Behrus Puladi, Moon Kim, Felix Busch, Felix Nensa, Daniel Truhn, Tianyu Han and Michael Forsting. Their work appears in journals such as Journal of the American Medical Informatics Association, Computer Methods and Programs in Biomedicine, European Radiology, Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz and Healthcare Analytics.
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.