Rawan AlSaad

27 papers receiving 624 citations

Hit Papers

Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook 2024 · 91 citations
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Peers

Rawan AlSaad
Comparison fields: 5 of 104
  • Health Informatics 277
  • Family Practice 38
  • Applied Psychology 80
  • Health Information Management 41
  • Experimental and Cognitive Psychology 78
Replace Sarah Aziz with:
Sarah Aziz Qatar
Mahmood Alzubaidi Qatar
Yan Fossat Canada
Laura Vardoulakis United States
Adam Palanica Canada
Samantha Winter United States
Anirudh Thommandram Canada
Ernest Lim United Kingdom
John Yap Singapore
Michael H. Li Canada
Rawan AlSaad relative to Sarah Aziz Qatar Sarah Aziz's profile →
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Citations per year

Countries citing papers authored by Rawan AlSaad

Since Specialization
Citations

This map shows the geographic impact of Rawan AlSaad'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 Rawan AlSaad with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rawan AlSaad more than expected).

Fields of papers citing papers by Rawan AlSaad

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Rawan AlSaad. 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 Rawan AlSaad. The network helps show where Rawan AlSaad may publish in the future.

Co-authors

The 25 scholars most cited alongside Rawan AlSaad, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Rawan AlSaad Line = papers co-authored together Rawan AlSaad links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 30 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Large Language Models in Medical Education: Opportunities, Challenges, and Future Directions
Hit paper breakdown →
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2
Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook
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202491
3 202355
4 202250
5 202420
6 202219
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9 202214
10 202313
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12 20249
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15 20233
16 20253
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20 20122

About Rawan AlSaad

Rawan AlSaad is a scholar working on Experimental and Cognitive Psychology, Artificial Intelligence, Pediatrics, Perinatology and Child Health, Cardiology and Cardiovascular Medicine and Public Health, Environmental and Occupational Health, having authored 30 papers that have together received 664 indexed citations. Recurring topics across this work include Digital Mental Health Interventions (4 papers), Topic Modeling (4 papers), Machine Learning in Healthcare (4 papers), Artificial Intelligence in Healthcare and Education (3 papers), Mental Health Research Topics (3 papers), COVID-19 diagnosis using AI (2 papers), Mental Health via Writing (2 papers) and Infant Development and Preterm Care (2 papers). The work is most often cited by research in Health Informatics (277 citations), Family Practice (38 citations), Applied Psychology (80 citations), Health Information Management (41 citations) and Experimental and Cognitive Psychology (78 citations). Rawan AlSaad has collaborated with scholars based in Qatar, United Arab Emirates and Saudi Arabia. Frequent co-authors include Alaa Abd‐Alrazaq, Arfan Ahmed, Javaid I. Sheikh, Sarah Aziz, Rafat Damseh, Dari Alhuwail, M Healy, Syed Latifi, Sabri Boughorbel and Qutaibah Malluhi. Their work appears in journals such as Journal of Medical Internet Research, Artificial Intelligence in Medicine, npj Digital Medicine, Scientific Reports and BioData Mining.

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.

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