Liam G. McCoy

1.2k total citations
25 papers, 464 citations indexed

About

Liam G. McCoy is a scholar working on Health Informatics, Artificial Intelligence and General Health Professions. According to data from OpenAlex, Liam G. McCoy has authored 25 papers receiving a total of 464 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Health Informatics, 8 papers in Artificial Intelligence and 7 papers in General Health Professions. Recurrent topics in Liam G. McCoy's work include Artificial Intelligence in Healthcare and Education (15 papers), Machine Learning in Healthcare (6 papers) and Healthcare cost, quality, practices (4 papers). Liam G. McCoy is often cited by papers focused on Artificial Intelligence in Healthcare and Education (15 papers), Machine Learning in Healthcare (6 papers) and Healthcare cost, quality, practices (4 papers). Liam G. McCoy collaborates with scholars based in United States, Canada and United Kingdom. Liam G. McCoy's co-authors include Leo Anthony Celi, Sunit Das, Vinyas Harish, Marzyeh Ghassemi, Judy Wawira Gichoya, Sujay Nagaraj, Felipe Morgado, Connor T. A. Brenna, Karina Vold and Arjun K. Manrai and has published in prestigious journals such as New England Journal of Medicine, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Liam G. McCoy

22 papers receiving 447 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Liam G. McCoy 256 142 107 81 46 25 464
Vinyas Harish 193 0.8× 110 0.8× 87 0.8× 71 0.9× 75 1.6× 26 444
Stephanie Teeple 228 0.9× 135 1.0× 108 1.0× 81 1.0× 37 0.8× 9 524
Piyush Mathur 254 1.0× 129 0.9× 78 0.7× 77 1.0× 59 1.3× 32 533
Lama Moukheiber 288 1.1× 139 1.0× 125 1.2× 102 1.3× 61 1.3× 12 599
David Lyell 196 0.8× 108 0.8× 63 0.6× 63 0.8× 84 1.8× 21 444
Christian Rose 182 0.7× 84 0.6× 81 0.8× 99 1.2× 39 0.8× 31 430
Mohammed Saeed 257 1.0× 198 1.4× 106 1.0× 116 1.4× 84 1.8× 4 577
Laleh Seyyed-Kalantari 311 1.2× 211 1.5× 246 2.3× 49 0.6× 35 0.8× 28 613
Fred Hersch 132 0.5× 143 1.0× 158 1.5× 81 1.0× 54 1.2× 16 562
Supawadee Suppadungsuk 290 1.1× 194 1.4× 85 0.8× 59 0.7× 38 0.8× 49 545

Countries citing papers authored by Liam G. McCoy

Since Specialization
Citations

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

Fields of papers citing papers by Liam G. McCoy

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Liam G. McCoy. 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 Liam G. McCoy. The network helps show where Liam G. McCoy may publish in the future.

Co-authorship network of co-authors of Liam G. McCoy

This figure shows the co-authorship network connecting the top 25 collaborators of Liam G. McCoy. A scholar is included among the top collaborators of Liam G. McCoy 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 Liam G. McCoy. Liam G. McCoy 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
1.
Gheihman, Galina, Tamara Kaplan, Liam G. McCoy, et al.. (2025). Education Research: Creating Online Interactive Case-Based Learning Experiences From Educational Case Reports With Large Language Models. PubMed. 4(4). e200250–e200250.
2.
McCoy, Liam G., Stephen Bacchi, Nigel CK Tan, et al.. (2025). Assessment of Large Language Models in Clinical Reasoning: A Novel Benchmarking Study. NEJM AI. 2(10). 3 indexed citations
3.
McCoy, Liam G., et al.. (2025). Parallel pressures: the common roots of doctor bullshit and large language model hallucinations. BMJ. 391. r2570–r2570. 1 indexed citations
4.
Charpignon, Marie‐Laure, Luis Filipe Nakayama, Jack Gallifant, et al.. (2025). Diversity in the medical research ecosystem: a descriptive scientometric analysis of over 49 000 studies and 150 000 authors published in high-impact medical journals between 2007 and 2022. BMJ Open. 15(1). e086982–e086982. 2 indexed citations
5.
McCoy, Liam G., Azra Bihorac, Leo Anthony Celi, et al.. (2025). Building health systems capable of leveraging AI: applying Paul Farmer’s 5S framework for equitable global health. PubMed. 3(1). 39–39. 3 indexed citations
6.
Cook, Benjamin K., Brandon Stretton, John Maddison, et al.. (2025). Use of Large Language Models for Rapid Quantitative Feedback in Case-Based Learning: A Pilot Study. Medical Science Educator. 35(3). 1169–1171. 3 indexed citations
7.
Haimovich, Adrian D., Alexander T. Janke, Keith E. Kocher, et al.. (2025). Managing Clinical Uncertainty: Formalizing Management Reasoning in Emergency Care Delivery. Annals of Emergency Medicine.
8.
McCoy, Liam G., et al.. (2024). Understanding and training for the impact of large language models and artificial intelligence in healthcare practice: a narrative review. BMC Medical Education. 24(1). 1096–1096. 13 indexed citations
9.
Gallifant, Jack, Danielle S. Bitterman, Leo Anthony Celi, et al.. (2024). Ethical debates amidst flawed healthcare artificial intelligence metrics. npj Digital Medicine. 7(1). 243–243. 4 indexed citations
10.
11.
Balagopalan, Aparna, Ioana Baldini, Leo Anthony Celi, et al.. (2024). Machine learning for healthcare that matters: Reorienting from technical novelty to equitable impact. SHILAP Revista de lepidopterología. 3(4). e0000474–e0000474. 8 indexed citations
12.
Gallifant, Jack, Amelia Fiske, Yulia A. Strekalova, et al.. (2024). Peer review of GPT-4 technical report and systems card. SHILAP Revista de lepidopterología. 3(1). e0000417–e0000417. 45 indexed citations
13.
Restrepo, David, Luis Filipe Nakayama, Chenwei Wu, et al.. (2024). Seeing Beyond Borders: Evaluating LLMs in Multilingual Ophthalmological Question Answering. 565–566. 1 indexed citations
14.
McCoy, Liam G., Arjun K. Manrai, & Adam Rodman. (2024). Large Language Models and the Degradation of the Medical Record. New England Journal of Medicine. 391(17). 1561–1564. 16 indexed citations
15.
16.
McCoy, Liam G., et al.. (2021). Believing in black boxes: machine learning for healthcare does not need explainability to be evidence-based. Journal of Clinical Epidemiology. 142. 252–257. 73 indexed citations
17.
Nestor, Bret, Liam G. McCoy, Amol A. Verma, et al.. (2020). Preparing a Clinical Support Model for Silent Mode in General Internal Medicine. 950–972. 5 indexed citations
18.
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
19.
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
20.
McCoy, Liam G., Jonathan Smith, Isha Berry, et al.. (2020). Characterizing early Canadian federal, provincial, territorial and municipal nonpharmaceutical interventions in response to COVID-19: a descriptive analysis. CMAJ Open. 8(3). E545–E553. 33 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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