Felipe Morgado

820 total citations
9 papers, 287 citations indexed

About

Felipe Morgado is a scholar working on Health Informatics, Atomic and Molecular Physics, and Optics and Family Practice. According to data from OpenAlex, Felipe Morgado has authored 9 papers receiving a total of 287 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Health Informatics, 3 papers in Atomic and Molecular Physics, and Optics and 2 papers in Family Practice. Recurrent topics in Felipe Morgado's work include Artificial Intelligence in Healthcare and Education (5 papers), Atomic and Subatomic Physics Research (3 papers) and Machine Learning in Healthcare (2 papers). Felipe Morgado is often cited by papers focused on Artificial Intelligence in Healthcare and Education (5 papers), Atomic and Subatomic Physics Research (3 papers) and Machine Learning in Healthcare (2 papers). Felipe Morgado collaborates with scholars based in Canada, United States and United Kingdom. Felipe Morgado's co-authors include Vinyas Harish, Sunit Das, Sujay Nagaraj, Liam G. McCoy, Leo Anthony Celi, Marcus J. Couch, Giles Santyr, Jonathan H. Rayment, Félix Ratjen and Nikhil Kanhere and has published in prestigious journals such as Magnetic Resonance in Medicine, Academic Medicine and Translational Psychiatry.

In The Last Decade

Felipe Morgado

9 papers receiving 276 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Felipe Morgado Canada	7	165	97	60	56	55	9	287
Ricky Hu Canada	11	162 1.0×	118 1.2×	27 0.5×	38 0.7×	84 1.5×	20	390
Rohit Srinivasan United Kingdom	7	254 1.5×	163 1.7×	40 0.7×	4 0.1×	71 1.3×	16	403
Sergey Morozov Netherlands	6	186 1.1×	171 1.8×	10 0.2×	10 0.2×	79 1.4×	10	282
Vincci Lui Canada	5	149 0.9×	42 0.4×	66 1.1×	6 0.1×	38 0.7×	8	318
Nanxi Zha Canada	9	9 0.1×	124 1.3×	33 0.6×	37 0.7×	68 1.2×	18	298
Thomas Krendl Gilbert United States	8	75 0.5×	25 0.3×	20 0.3×	4 0.1×	81 1.5×	17	282
William K. Antwi Ghana	9	117 0.7×	181 1.9×	34 0.6×	2 0.0×	37 0.7×	48	344
Patrick Weber United Kingdom	8	142 0.9×	44 0.5×	50 0.8×	10 0.2×	60 1.1×	14	301
Sunny S. Lou United States	11	17 0.1×	21 0.2×	66 1.1×	10 0.2×	16 0.3×	32	430
Melissa P. Culp United States	6	70 0.4×	111 1.1×	47 0.8×	2 0.0×	25 0.5×	16	191

Countries citing papers authored by Felipe Morgado

Since Specialization

Citations

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

Fields of papers citing papers by Felipe Morgado

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Felipe Morgado

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

All Works

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9 of 9 papers shown

Morgado, Felipe, Marlee M. Vandewouw, Christopher Hammill, et al.. (2024). Behaviour-correlated profiles of cerebellar-cerebral functional connectivity observed in independent neurodevelopmental disorder cohorts. Translational Psychiatry. 14(1). 173–173. 2 indexed citations

Harish, Vinyas, et al.. (2022). Ignorance Isn't Bliss: We Must Close the Machine Learning Knowledge Gap in Pediatric Critical Care. Frontiers in Pediatrics. 10. 864755–864755. 3 indexed citations

Harish, Vinyas, et al.. (2021). Knowledge and Attitudes on Artificial Intelligence in Healthcare: A Provincial Survey Study of Medical Students. MedEdPublish. 10(1). 41 indexed citations

McCoy, Liam G., Sujay Nagaraj, Felipe Morgado, et al.. (2020). What do medical students actually need to know about artificial intelligence?. npj Digital Medicine. 3(1). 86–86. 133 indexed citations

Harish, Vinyas, Felipe Morgado, Ariel Dora Stern, & Sunit Das. (2020). Artificial Intelligence and Clinical Decision Making: The New Nature of Medical Uncertainty. Academic Medicine. 96(1). 31–36. 35 indexed citations

Nagaraj, Sujay, Vinyas Harish, Liam G. McCoy, et al.. (2020). From Clinic to Computer and Back Again: Practical Considerations When Designing and Implementing Machine Learning Solutions for Pediatrics. Current Treatment Options in Pediatrics. 6(4). 336–349. 6 indexed citations

Couch, Marcus J., Felipe Morgado, Nikhil Kanhere, et al.. (2019). Assessing the feasibility of hyperpolarized ¹²⁹Xe multiple‐breath washout MRI in pediatric cystic fibrosis. Magnetic Resonance in Medicine. 84(1). 304–311. 16 indexed citations

Santyr, Giles, Nikhil Kanhere, Felipe Morgado, et al.. (2018). Hyperpolarized Gas Magnetic Resonance Imaging of Pediatric Cystic Fibrosis Lung Disease. Academic Radiology. 26(3). 344–354. 45 indexed citations

Morgado, Felipe, et al.. (2018). Effect of T₁ relaxation on ventilation mapping using hyperpolarized ¹²⁹Xe multiple breath wash‐out imaging. Magnetic Resonance in Medicine. 80(6). 2670–2680. 6 indexed citations

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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