Byron Crowe

910 total citations · 1 hit paper
16 papers, 534 citations indexed

About

Byron Crowe is a scholar working on Health Informatics, Epidemiology and Artificial Intelligence. According to data from OpenAlex, Byron Crowe has authored 16 papers receiving a total of 534 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Health Informatics, 3 papers in Epidemiology and 3 papers in Artificial Intelligence. Recurrent topics in Byron Crowe's work include Artificial Intelligence in Healthcare and Education (4 papers), Machine Learning in Healthcare (3 papers) and Obesity and Health Practices (3 papers). Byron Crowe is often cited by papers focused on Artificial Intelligence in Healthcare and Education (4 papers), Machine Learning in Healthcare (3 papers) and Obesity and Health Practices (3 papers). Byron Crowe collaborates with scholars based in United States, Japan and Germany. Byron Crowe's co-authors include Adam Rodman, Zahir Kanjee, Juan M. Bustamante, Rick L. Tarleton, Ray E. Gleason, J I Mann, John A. Baron, Raja-Elie E. Abdulnour, Stephanie Cabral and Philip Wilson and has published in prestigious journals such as JAMA, The Journal of Infectious Diseases and Annals of the Rheumatic Diseases.

In The Last Decade

Byron Crowe

16 papers receiving 517 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Accuracy of a Generative Artificial Intelligence Model in a Complex Diagnostic Challenge

2023 224 citations Zahir Kanjee, Byron Crowe et al. JAMA profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Byron Crowe United States	7	204	200	134	118	80	16	534
Vinyas Harish Canada	9	71 0.3×	193 1.0×	36 0.3×	110 0.9×	87 1.1×	26	444
Jackson Steinkamp United States	12	73 0.4×	60 0.3×	39 0.3×	90 0.8×	68 0.8×	23	339
Victoria Goode United States	4	103 0.5×	14 0.1×	55 0.4×	21 0.2×	76 0.9×	6	358
Gwendolyn C. Murphy United States	9	142 0.7×	18 0.1×	14 0.1×	44 0.4×	57 0.7×	16	402
Joseph Alexander Paguio United States	5	62 0.3×	138 0.7×	71 0.5×	58 0.5×	65 0.8×	13	351
Susan Curtis United States	8	113 0.6×	205 1.0×	13 0.1×	53 0.4×	56 0.7×	13	352
Atul Nakhasi United States	5	32 0.2×	16 0.1×	61 0.5×	31 0.3×	59 0.7×	7	270
Friederike Holderried Germany	11	132 0.6×	88 0.4×	9 0.1×	26 0.2×	31 0.4×	21	328
Laura Shafner United States	5	36 0.2×	95 0.5×	44 0.3×	43 0.4×	39 0.5×	7	344
Muhammad Saqib Pakistan	9	55 0.3×	72 0.4×	37 0.3×	31 0.3×	56 0.7×	27	339

Countries citing papers authored by Byron Crowe

Since Specialization

Citations

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

Fields of papers citing papers by Byron Crowe

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Byron Crowe

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

All Works

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16 of 16 papers shown

Buckley, Thomas A., Byron Crowe, Raja-Elie E. Abdulnour, Adam Rodman, & Arjun K. Manrai. (2025). Comparison of Frontier Open-Source and Proprietary Large Language Models for Complex Diagnoses. JAMA Health Forum. 6(3). e250040–e250040. 5 indexed citations

Crowe, Byron, et al.. (2024). Overcoming clinical inertia with SGLT‐2 inhibitors: Strategies to accelerate adoption in heart failure. Journal of Hospital Medicine. 20(1). 67–70. 1 indexed citations

Rodman, Adam, et al.. (2024). Harnessing Generative Artificial Intelligence for Medical Education. Academic Medicine. 100(1). 116–116. 1 indexed citations

Cabral, Stephanie, Zahir Kanjee, Philip Wilson, et al.. (2024). Clinical Reasoning of a Generative Artificial Intelligence Model Compared With Physicians. JAMA Internal Medicine. 184(5). 581–581. 57 indexed citations

Crowe, Byron, et al.. (2023). How Design Thinking and Quality Improvement Can Be Integrated into a “Human-Centered Quality Improvement” Approach to Solve Problems in Perinatology. Clinics in Perinatology. 50(2). 435–448. 3 indexed citations

Kanjee, Zahir, Byron Crowe, & Adam Rodman. (2023). Accuracy of a Generative Artificial Intelligence Model in a Complex Diagnostic Challenge. JAMA. 330(1). 78–78. 224 indexed citations breakdown →

Morand, Eric F., R. Furie, Edward M Vital, et al.. (2022). POS0190 EFFICACY AND SAFETY OF BARICITINIB IN PATIENTS WITH SYSTEMIC LUPUS ERYTHEMATOSUS: RESULTS FROM TWO RANDOMISED, DOUBLE-BLIND, PLACEBO-CONTROLLED, PARALLEL-GROUP, PHASE 3 STUDIES. Annals of the Rheumatic Diseases. 81. 327–328. 6 indexed citations

Golovaty, Ilya, et al.. (2021). Reach, engagement and effectiveness of in-person and online lifestyle change programs to prevent diabetes. BMC Public Health. 21(1). 1314–1314. 14 indexed citations

Crowe, Byron, et al.. (2021). To improve quality, leverage design. BMJ Quality & Safety. 31(1). 70–74. 17 indexed citations

10.

Crowe, Byron, et al.. (2021). Missed Opportunity for Fall Prevention. JAMA Internal Medicine. 181(5). 689–689. 1 indexed citations

11.

Dörner, Thomas, Yoshiya Tanaka, Michelle Petri, et al.. (2020). OP0045 DELINEATION OF A PROINFLAMMATORY CYTOKINE PROFILE TARGETED BY JAK1/2 INHIBITION USING BARICITINIB IN A PHASE 2 SLE TRIAL. Annals of the Rheumatic Diseases. 79. 29–30. 1 indexed citations

12.

Sauder, Katherine A., et al.. (2020). Participation and weight loss in online National Diabetes Prevention Programs: a comparison of age and gender subgroups. Translational Behavioral Medicine. 11(2). 342–350. 17 indexed citations

13.

Crowe, Byron, et al.. (2020). Things We Do for No Reason^™: Routine Correction of Elevated INR and Thrombocytopenia Prior to Paracentesis in Patients with Cirrhosis. Journal of Hospital Medicine. 16(2). 102–104. 4 indexed citations

14.

Bustamante, Juan M., et al.. (2013). New, Combined, and Reduced Dosing Treatment Protocols Cure Trypanosoma cruzi Infection in Mice. The Journal of Infectious Diseases. 209(1). 150–162. 109 indexed citations

15.

Crowe, Byron, et al.. (2013). Computational and synthetic studies towards improving rescinnamine as an inducer of MSH2-dependent apoptosis in cancer treatment.. PubMed. 1(1). 2 indexed citations

16.

Baron, John A., Ray E. Gleason, Byron Crowe, & J I Mann. (1990). Preliminary trial of the effect of general practice based nutritional advice.. PubMed. 40(333). 137–41. 72 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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