Dev Dash

1.3k citations
10 papers · 277 · 1 hit paper · h-index 6

Impact in

Papers in

Dev Dash

10 papers receiving 272 citations

Dev Dash's Hit Papers

Testing and Evaluation of Health Care Applications of Large Language Models 2024 · 193 citations
1930+1Years since publication50100150

Peers

Dev Dash
Comparison fields: 5 of 48
  • Health Informatics 108
  • Family Practice 11
  • Health Information Management 18
  • Radiology, Nuclear Medicine and Imaging 32
  • Artificial Intelligence 47
Replace Cesar A. Gomez-Cabello with:
Cesar A. Gomez-Cabello United States
Sophia M. Pressman United States
Masashi Yokose Japan
Robbie Holland Germany
Suhana Bedi United States
Erik Drysdale Canada
Shawheen J. Rezaei United States
Akash Chaurasia United States
Graham Cole United Kingdom
Elliott H Taylor United Kingdom
Dev Dash relative to Cesar A. Gomez-Cabello United States Cesar A. Gomez-Cabello's profile →
Citations per field
00.5×1.6×
Cesar A. Gomez-Cabello · 1×
Citations per year

Countries citing papers authored by Dev Dash

Since Specialization
Citations

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

Fields of papers citing papers by Dev Dash

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Dev Dash, 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 Dev Dash Line = papers co-authored together Dev Dash links everyone, so they are left out of the graph.

All Works

10 of 10 papers shown
#Work
1
Testing and Evaluation of Health Care Applications of Large Language Models
Hit paper breakdown →
2024193
2 202230
3 202423
4 202210
5 20236
6 20226
7 20243
8 20253
9 20242
10 20221

About Dev Dash

Dev Dash is a scholar working on Health Informatics, Artificial Intelligence, Neurology, Critical Care and Intensive Care Medicine and Health Information Management, having authored 10 papers that have together received 277 indexed citations. Recurring topics across this work include Artificial Intelligence in Healthcare and Education (3 papers), Intracerebral and Subarachnoid Hemorrhage Research (2 papers), Machine Learning in Healthcare (2 papers), Artificial Intelligence in Healthcare (1 paper), Emergency and Acute Care Studies (1 paper), Ultrasound in Clinical Applications (1 paper), Topic Modeling (1 paper) and Explainable Artificial Intelligence (XAI) (1 paper). The work is most often cited by research in Health Informatics (108 citations), Family Practice (11 citations), Health Information Management (18 citations), Radiology, Nuclear Medicine and Imaging (32 citations) and Artificial Intelligence (47 citations). Dev Dash has collaborated with scholars based in United States and Thailand. Frequent co-authors include Nigam H. Shah, Michael A. Pfeffer, Alison Callahan, Arnold Milstein, Oluwasanmi Koyejo, Mehr Kashyap, Lisa Soleymani Lehmann, Hyo Jung Hong, Michael Wornow and Karandeep Singh. Their work appears in journals such as JAMA, JAMA Network Open, Journal of Emergency Medicine, Annals of Emergency Medicine and Applied Clinical Informatics.

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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