Junaid Bajwa
- Health Informatics top 0.2%
- Radiology, Nuclear Medicine and Imaging top 10%
- Artificial Intelligence top 10%
- Health Information Management top 2%
- Public Health, Environmental and Occupational Health
- Co-authors
- Aditya NoriBryan WilliamsUsman MunirRoy JonesPeter PassmoreCraig RitchieGlen ColemanKempe Ronald Hope
- Topics
- Ginkgo biloba and Cashew Applications (1 paper)Artificial Intelligence in Healthcare (1 paper)Cholinesterase and Neurodegenerative Diseases (1 paper)
- Journals
- The journal of nutrition health & agingFuture Healthcare JournalInnovAiT Education and inspiration for general practice
- Partner nations
- United KingdomUnited States
In The Last Decade
Junaid Bajwa
2 papers receiving 656 citations
Hit Papers
Peers
Comparison fields: 5 of 122
- Health Informatics 379
- Radiology, Nuclear Medicine and Imaging 206
- Artificial Intelligence 125
- Health Information Management 89
- Public Health, Environmental and Occupational Health 85
Countries citing papers authored by Junaid Bajwa
This map shows the geographic impact of Junaid Bajwa'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 Junaid Bajwa with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Junaid Bajwa more than expected).
Fields of papers citing papers by Junaid Bajwa
This network shows the impact of papers produced by Junaid Bajwa. 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 Junaid Bajwa. The network helps show where Junaid Bajwa may publish in the future.
Co-authorship network of co-authors of Junaid Bajwa
This figure shows the co-authorship network connecting the top 25 collaborators of Junaid Bajwa. A scholar is included among the top collaborators of Junaid Bajwa 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 Junaid Bajwa. Junaid Bajwa is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Artificial intelligence in healthcare: transforming the practice of medicinebreakdown → | 668 |
| 2 | 15 | |
| 3 | 0 | |
| 4 | 0 |
About Junaid Bajwa
Junaid Bajwa is a scholar working on Health Informatics, Otorhinolaryngology and Health Information Management, having authored 4 papers that have together received 683 indexed citations. Recurring topics across this work include Ginkgo biloba and Cashew Applications (1 paper), Artificial Intelligence in Healthcare (1 paper) and Cholinesterase and Neurodegenerative Diseases (1 paper). The work is most often cited by research in Health Informatics (379 citations), Health Information Management (89 citations) and Radiology, Nuclear Medicine and Imaging (206 citations). Junaid Bajwa has collaborated with scholars based in United Kingdom and United States. Frequent co-authors include Aditya Nori, Bryan Williams, Usman Munir, Roy Jones, Peter Passmore, Craig Ritchie, Glen Coleman, Kempe Ronald Hope, Michael Lawton and Frédéric M. Vaz. Their work appears in journals such as The journal of nutrition health & aging, Future Healthcare Journal and InnovAiT Education and inspiration for general practice.
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.