Laura Gutiérrez

3.4k citations
20 papers · 1.7k indexed · 1 hit paper · h-index 8
Topics
Artificial Intelligence in Healthcare and Education (5 papers)Radiomics and Machine Learning in Medical Imaging (4 papers)Machine Learning in Healthcare (3 papers)

In The Last Decade

Laura Gutiérrez

17 papers receiving 1.7k citations

Hit Papers

Large language models in medicine2023202620242025202350010001.5k

Peers

Laura Gutiérrez
Comparison fields: 5 of 166
  • Health Informatics 756
  • Artificial Intelligence 687
  • Radiology, Nuclear Medicine and Imaging 476
  • Molecular Biology 135
  • General Health Professions 107
Replace Ting Fang Tan with:
Ting Fang Tan Singapore
Arun James Thirunavukarasu United Kingdom
Kabilan Elangovan Singapore
Oishi Banerjee United States
Mustafa Suleyman United Kingdom
Jiming Xu China
Claire Cui United States
Hui Zhi China
Fei Jiang United States
Imon Banerjee United States
Laura Gutiérrez relative to Ting Fang Tan Singapore Ting Fang Tan's profile →
Citations per field
00.5×4.5×
Ting Fang Tan · 1×
Citations per year

Countries citing papers authored by Laura Gutiérrez

Since Specialization
Citations

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

Fields of papers citing papers by Laura Gutiérrez

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Laura Gutiérrez

This figure shows the co-authorship network connecting the top 25 collaborators of Laura Gutiérrez. A scholar is included among the top collaborators of Laura Gutiérrez 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 Laura Gutiérrez. Laura Gutiérrez is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 0
2 0
3 0
4 21
5 2
6 2
7 17
8
Large language models in medicinebreakdown →
1541
9 1
10 9
11 39
12 1
13 44
14 1
15 1
16
Registro de Trauma Ocular Colombiano (ReTOC) – Primer reporte.
1
17 25
18 1
19 18
20 2

About Laura Gutiérrez

Laura Gutiérrez is a scholar working on Health Informatics, Radiology, Nuclear Medicine and Imaging and Ophthalmology, having authored 20 papers that have together received 1.7k indexed citations. Recurring topics across this work include Artificial Intelligence in Healthcare and Education (5 papers), Radiomics and Machine Learning in Medical Imaging (4 papers) and Machine Learning in Healthcare (3 papers). The work is most often cited by research in Health Informatics (756 citations), Family Practice (70 citations) and Health Information Management (100 citations). Laura Gutiérrez has collaborated with scholars based in Singapore, Spain and United States. Frequent co-authors include Daniel Shu Wei Ting, Arun James Thirunavukarasu, Darren Shu Jeng Ting, Ting Fang Tan, Kabilan Elangovan, Karen Castañeda, Wei Yan Ng, Omar Sánchez, Allan Fong and Jane Lim. Their work appears in journals such as Nature Medicine, American Journal of Respiratory and Critical Care Medicine and Journal of Medical Internet Research.

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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