Michelle Yip

980 total citations · 1 hit paper
14 papers, 605 citations indexed

About

Michelle Yip is a scholar working on Radiology, Nuclear Medicine and Imaging, Ophthalmology and Health Information Management. According to data from OpenAlex, Michelle Yip has authored 14 papers receiving a total of 605 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Radiology, Nuclear Medicine and Imaging, 8 papers in Ophthalmology and 4 papers in Health Information Management. Recurrent topics in Michelle Yip's work include Retinal Imaging and Analysis (11 papers), Retinal and Optic Conditions (5 papers) and Artificial Intelligence in Healthcare (4 papers). Michelle Yip is often cited by papers focused on Retinal Imaging and Analysis (11 papers), Retinal and Optic Conditions (5 papers) and Artificial Intelligence in Healthcare (4 papers). Michelle Yip collaborates with scholars based in Singapore, United Kingdom and China. Michelle Yip's co-authors include Daniel Shu Wei Ting, Tien Yin Wong, Gavin Siew Wei Tan, Mong Li Lee, Yuchen Xie, Haslina Hamzah, Quang D. Nguyen, Valentina Bellemo, Wynne Hsu and Gilbert Lim and has published in prestigious journals such as Investigative Ophthalmology & Visual Science, British Journal of Social Psychology and npj Digital Medicine.

In The Last Decade

Michelle Yip

14 papers receiving 594 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.

Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: a clinical validation study

2019 227 citations Valentina Bellemo, Zhan Wei Lim et al. The Lancet Digital Health profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Michelle Yip Singapore	9	447	363	84	82	72	14	605
Wei Yan Ng Singapore	16	454 1.0×	504 1.4×	44 0.5×	16 0.2×	48 0.7×	22	715
Louis R. Pasquale United States	4	542 1.2×	455 1.3×	101 1.2×	36 0.4×	67 0.9×	4	753
Lanqin Zhao China	16	461 1.0×	400 1.1×	61 0.7×	16 0.2×	105 1.5×	34	632
Valentina Bellemo Singapore	7	511 1.1×	399 1.1×	91 1.1×	112 1.4×	30 0.4×	13	635
Linda A. Lam United States	6	368 0.8×	415 1.1×	44 0.5×	19 0.2×	47 0.7×	10	609
Warren Clarida United States	3	675 1.5×	533 1.5×	57 0.7×	99 1.2×	27 0.4×	4	781
David Coz United States	4	189 0.4×	106 0.3×	82 1.0×	43 0.5×	21 0.3×	5	362
Luis Filipe Nakayama Brazil	11	147 0.3×	123 0.3×	78 0.9×	33 0.4×	18 0.3×	54	348
Abigail E. Huang United States	8	225 0.5×	172 0.5×	39 0.5×	47 0.6×	15 0.2×	14	377
Hon Tym Wong Singapore	11	437 1.0×	487 1.3×	32 0.4×	34 0.4×	30 0.4×	18	592

Countries citing papers authored by Michelle Yip

Since Specialization

Citations

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

Fields of papers citing papers by Michelle Yip

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michelle Yip

This figure shows the co-authorship network connecting the top 25 collaborators of Michelle Yip. A scholar is included among the top collaborators of Michelle Yip 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 Michelle Yip. Michelle Yip 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:

14 of 14 papers shown

Poh, Stanley, Michelle Yip, Andrew Tsai, et al.. (2024). Artificial Intelligence, Digital Imaging, and Robotics Technologies for Surgical Vitreoretinal Diseases. Ophthalmology Retina. 8(7). 633–645. 11 indexed citations

Tsai, Andrew, Michelle Yip, Gavin Siew Wei Tan, et al.. (2024). Implementation of Artificial Intelligence in Retinopathy of Prematurity Care: Challenges and Opportunities. International Ophthalmology Clinics. 64(4). 9–14. 1 indexed citations

Li, Yong, Michelle Yip, Yilin Ning, et al.. (2023). Topical Atropine for Childhood Myopia Control. JAMA Ophthalmology. 142(1). 15–15. 37 indexed citations

Gutiérrez, Laura, Jane Lim, Li Lian Foo, et al.. (2022). Application of artificial intelligence in cataract management: current and future directions. Eye and Vision. 9(1). 3–3. 44 indexed citations

Yip, Michelle, Zhaoran Wang, Anna C. S. Tan, et al.. (2021). Digital Technology for AMD Management in the Post-COVID-19 New Normal. Asia-Pacific Journal of Ophthalmology. 10(1). 39–48. 18 indexed citations

Yip, Michelle, Gilbert Lim, Zhan Wei Lim, et al.. (2020). Technical and imaging factors influencing performance of deep learning systems for diabetic retinopathy. npj Digital Medicine. 3(1). 40–40. 31 indexed citations

Xie, Yuchen, Quang D. Nguyen, Haslina Hamzah, et al.. (2020). Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study. The Lancet Digital Health. 2(5). e240–e249. 174 indexed citations

Yip, Michelle, Gilbert Lim, Valentina Bellemo, et al.. (2019). Effect of Image Compression and Number of Fields on a Deep Learning System for Detection of Diabetic Retinopathy. Investigative Ophthalmology & Visual Science. 60(9). 1438–1438. 1 indexed citations

Bellemo, Valentina, Zhan Wei Lim, Gilbert Lim, et al.. (2019). The Application of Deep Learning System to Screen for Diabetic Retinopathy in an Underprivileged African Population with Diabetes. Investigative Ophthalmology & Visual Science. 60(9). 1439–1439. 1 indexed citations

10.

Xie, Yuchen, Quang D. Nguyen, Valentina Bellemo, et al.. (2019). Cost-Effectiveness Analysis of an Artificial Intelligence-Assisted Deep Learning System Implemented in the National Tele-Medicine Diabetic Retinopathy Screening in Singapore. Investigative Ophthalmology & Visual Science. 60(9). 5471–5471. 8 indexed citations

11.

Bellemo, Valentina, Zhan Wei Lim, Gilbert Lim, et al.. (2019). Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: a clinical validation study. The Lancet Digital Health. 1(1). e35–e44. 227 indexed citations breakdown →

12.

Bellemo, Valentina, Zhan Wei Lim, Gilbert Lim, et al.. (2019). Artificial Intelligence Using Deep Learning to Screen for Referable and Vision-Threatening Diabetic Retinopathy in Africa. SSRN Electronic Journal. 8 indexed citations

13.

Xie, Yuchen, Quang D. Nguyen, Gilbert Lim, et al.. (2019). Artificial Intelligence for Teleophthalmology-Based Diabetic Retinopathy Screening in a National Program: A Modelledn Economic Analysis Study. SSRN Electronic Journal. 2 indexed citations

14.

Sevdalis, Nick, Nigel Harvey, & Michelle Yip. (2006). Regret triggers inaction inertia – but which regret and how?. British Journal of Social Psychology. 45(4). 839–853. 42 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.

Explore authors with similar magnitude of impact