Roxana Daneshjou

5.6k total citations · 6 hit papers
77 papers, 2.6k citations indexed

About

Roxana Daneshjou is a scholar working on Oncology, Artificial Intelligence and Health Informatics. According to data from OpenAlex, Roxana Daneshjou has authored 77 papers receiving a total of 2.6k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Oncology, 27 papers in Artificial Intelligence and 23 papers in Health Informatics. Recurrent topics in Roxana Daneshjou's work include Cutaneous Melanoma Detection and Management (27 papers), Artificial Intelligence in Healthcare and Education (23 papers) and AI in cancer detection (18 papers). Roxana Daneshjou is often cited by papers focused on Cutaneous Melanoma Detection and Management (27 papers), Artificial Intelligence in Healthcare and Education (23 papers) and AI in cancer detection (18 papers). Roxana Daneshjou collaborates with scholars based in United States, United Kingdom and Canada. Roxana Daneshjou's co-authors include Russ B. Altman, Nicholas P. Tatonetti, James Zou, Veronica Rotemberg, Jesutofunmi A. Omiye, David Ouyang, Konrad J. Karczewski, Daniel E. Ho, Kevin Wu and Eric Q. Wu and has published in prestigious journals such as Nature Medicine, SHILAP Revista de lepidopterología and Blood.

In The Last Decade

Roxana Daneshjou

65 papers receiving 2.5k citations

Hit Papers

5 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.

Data-Driven Prediction of Drug Effects and Interactions

2012 610 citations Roxana Daneshjou, Russ B. Altman et al. profile →
How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals

2021 275 citations Roxana Daneshjou, James Zou et al. Nature Medicine profile →
Lack of Transparency and Potential Bias in Artificial Intelligence Data Sets and Algorithms

2021 198 citations Roxana Daneshjou, Mary Sun et al. JAMA Dermatology profile →
Large language models propagate race-based medicine

2023 186 citations Jesutofunmi A. Omiye, Jenna Lester et al. npj Digital Medicine profile →
Large Language Models in Medicine: The Potentials and Pitfalls

2024 131 citations Jesutofunmi A. Omiye, Haiwen Gui et al. Annals of Internal Medicine profile →
An evaluation framework for clinical use of large language models in patient interaction tasks

2025 48 citations Zhuo Ran Cai, Roxana Daneshjou et al. Nature Medicine profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Roxana Daneshjou United States	24	700	690	587	482	366	77	2.6k
Wei‐Qi Wei United States	25	680 1.0×	393 0.6×	1.1k 1.8×	196 0.4×	206 0.6×	100	4.1k
Jianying Hu United States	25	1.0k 1.4×	338 0.5×	590 1.0×	347 0.7×	61 0.2×	77	2.6k
Khader Shameer United States	29	495 0.7×	402 0.6×	1.1k 1.9×	293 0.6×	180 0.5×	83	3.7k
Rae Woong Park South Korea	28	763 1.1×	148 0.2×	628 1.1×	112 0.2×	218 0.6×	191	3.7k
Kenneth Jung United States	19	409 0.6×	314 0.5×	879 1.5×	97 0.2×	372 1.0×	27	2.5k
Anita Burgun France	32	1.5k 2.1×	156 0.2×	1.6k 2.8×	129 0.3×	186 0.5×	206	3.7k
Qingyu Chen China	27	895 1.3×	244 0.4×	835 1.4×	76 0.2×	202 0.6×	134	3.0k
Jeremy L. Warner United States	27	537 0.8×	126 0.2×	725 1.2×	85 0.2×	731 2.0×	166	2.7k
Guergana Savova United States	40	3.0k 4.2×	298 0.4×	2.4k 4.0×	90 0.2×	135 0.4×	139	5.4k
Ju Han Kim South Korea	32	296 0.4×	74 0.1×	1.6k 2.8×	92 0.2×	427 1.2×	244	4.0k

Countries citing papers authored by Roxana Daneshjou

Since Specialization

Citations

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

Fields of papers citing papers by Roxana Daneshjou

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Roxana Daneshjou

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Sharma, S., et al.. (2025). A longitudinal analysis of declining medical safety messaging in generative AI models. npj Digital Medicine. 8(1). 592–592. 1 indexed citations

Chang, Crystal, et al.. (2025). Evaluating anti-LGBTQIA+ medical bias in large language models. PLOS Digital Health. 4(9). e0001001–e0001001.

Omiye, Jesutofunmi A., et al.. (2025). Automated Detection of Benign and Malignant Skin Lesions from Reflectance Confocal Microscopy Images Using Deep Learning. JID Innovations. 5(6). 100404–100404.

Gui, Haiwen, et al.. (2025). Artificial Intelligence Use in Acne Diagnosis and Management—A Scoping Review. International Journal of Dermatology. 65(3). 437–443.

Daneshjou, Roxana, et al.. (2024). Evaluating the appropriateness of skin cancer prevention recommendations obtained from an online chat-based artificial intelligence model. JAAD International. 18. 145–147.

Maleki, Farhad, Linda Moy, Reza Forghani, et al.. (2024). RIDGE: Reproducibility, Integrity, Dependability, Generalizability, and Efficiency Assessment of Medical Image Segmentation Models. Journal of Imaging Informatics in Medicine. 38(4). 2524–2536. 2 indexed citations

Omiye, Jesutofunmi A., Haiwen Gui, Shawheen J. Rezaei, James Zou, & Roxana Daneshjou. (2024). Large Language Models in Medicine: The Potentials and Pitfalls. Annals of Internal Medicine. 177(2). 210–220. 131 indexed citations breakdown →

Babatunde, Abdulhammed Opeyemi, et al.. (2024). A scoping review of reporting gaps in FDA-approved AI medical devices. npj Digital Medicine. 7(1). 273–273. 58 indexed citations

Groh, Matthew, et al.. (2024). Deep learning-aided decision support for diagnosis of skin disease across skin tones. Nature Medicine. 30(2). 573–583. 48 indexed citations

10.

Gui, Haiwen, Jesutofunmi A. Omiye, Crystal Chang, & Roxana Daneshjou. (2024). The Promises and Perils of Foundation Models in Dermatology. Journal of Investigative Dermatology. 144(7). 1440–1448. 10 indexed citations

11.

Chang, Crystal, et al.. (2024). DDI-2: A Diverse Skin Condition Image Dataset Representing Self-Identified Asian Patients. Journal of Investigative Dermatology. 145(5). 1205–1208.e4. 1 indexed citations

12.

DeGrave, Alex J., et al.. (2024). Transparent medical image AI via an image–text foundation model grounded in medical literature. Nature Medicine. 30(4). 1154–1165. 36 indexed citations

13.

DeGrave, Alex J., Zhuo Ran Cai, Joseph D. Janizek, Roxana Daneshjou, & Su‐In Lee. (2023). Auditing the inference processes of medical-image classifiers by leveraging generative AI and the expertise of physicians. Nature Biomedical Engineering. 9(3). 294–306. 27 indexed citations

14.

Tadesse, Girmaw Abebe, Celia Cintas, Kush R. Varshney, et al.. (2023). Skin Tone Analysis for Representation in Educational Materials (STAR-ED) using machine learning. npj Digital Medicine. 6(1). 151–151. 12 indexed citations

15.

Daneshjou, Roxana, et al.. (2023). Recommendations for the use of pediatric data in artificial intelligence and machine learning ACCEPT-AI. npj Digital Medicine. 6(1). 166–166. 42 indexed citations

16.

Schwartz, Ilan S., et al.. (2023). Black Box Warning: Large Language Models and the Future of Infectious Diseases Consultation. Clinical Infectious Diseases. 78(4). 860–866. 64 indexed citations

17.

Daneshjou, Roxana, et al.. (2021). Lack of Transparency and Potential Bias in Artificial Intelligence Data Sets and Algorithms. JAMA Dermatology. 157(11). 1362–1362. 198 indexed citations breakdown →

18.

Daneshjou, Roxana, Leonid Shmuylovich, Ayman Grada, & Valerie Horsley. (2021). Research Techniques Made Simple: Scientific Communication using Twitter. Journal of Investigative Dermatology. 141(7). 1615–1621.e1. 5 indexed citations

19.

Vodrahalli, Kailas, Roxana Daneshjou, Roberto A. Novoa, et al.. (2020). TrueImage: A Machine Learning Algorithm to Improve the Quality of Telehealth Photos. PubMed. 26. 220–231. 16 indexed citations

20.

Gottlieb, Assaf, Roxana Daneshjou, Marianne K. DeGorter, et al.. (2017). Cohort-specific imputation of gene expression improves prediction of warfarin dose for African Americans. Genome Medicine. 9(1). 98–98. 8 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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