M Healy

10 papers receiving 497 citations

M Healy's Hit Papers

Large Language Models in Medical Education: Opportunities, Challenges, and Future Directions 2023 · 379 citations
3790+1+2Years since publication100200300

Peers

M Healy
Comparison fields: 5 of 81
  • Health Informatics 244
  • Family Practice 21
  • Computer Science Applications 27
  • Health Information Management 17
  • Radiology, Nuclear Medicine and Imaging 61
Replace Lama Moukheiber with:
Lama Moukheiber United States
Priya S. Garg United States
Kay Li Canada
Madiha Sajjad Pakistan
Syed Latifi Canada
Dana Moukheiber United States
Luigi De Angelis Italy
Brian C. Gin United States
María José Rementeria Spain
M Healy relative to Lama Moukheiber United States Lama Moukheiber's profile →
Citations per field
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Citations per year

Countries citing papers authored by M Healy

Since Specialization
Citations

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

Fields of papers citing papers by M Healy

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

11 of 11 papers shown
#Work
1
Large Language Models in Medical Education: Opportunities, Challenges, and Future Directions
Hit paper breakdown →
2023379
2 201565
3 199524
4 201518
5 201417
6
Towards a Framework for Defining and Categorising Business Process-As-A-Service (BPaaS)
201411
7 20163
8 20112
9 20062
10
Care of type 2 diabetes in unresourced general practice: current practice in the Mid-West.
20141
11 20250

About M Healy

M Healy is a scholar working on Public Health, Environmental and Occupational Health, Surgery, Computer Networks and Communications, Genetics and General Health Professions, having authored 11 papers that have together received 522 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (1 paper), Epilepsy research and treatment (1 paper), Business Process Modeling and Analysis (1 paper), Primary Care and Health Outcomes (1 paper), Parallel Computing and Optimization Techniques (1 paper), Service-Oriented Architecture and Web Services (1 paper), Transplantation: Methods and Outcomes (1 paper) and Artificial Intelligence in Healthcare and Education (1 paper). The work is most often cited by research in Health Informatics (244 citations), Family Practice (21 citations), Computer Science Applications (27 citations), Health Information Management (17 citations) and Radiology, Nuclear Medicine and Imaging (61 citations). M Healy has collaborated with scholars based in Ireland, Canada and Qatar. Frequent co-authors include Javaid I. Sheikh, Rawan AlSaad, Sarah Aziz, Dari Alhuwail, Rafat Damseh, Arfan Ahmed, Alaa Abd‐Alrazaq, Syed Latifi, Mary McCormack and Rose Anne Kenny. Their work appears in journals such as JMIR Medical Education, Diabetes Research and Clinical Practice, Critical Care, Public Health Nutrition and Diabetes Metabolic Syndrome and Obesity.

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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