Michael Ko

1.1k total citations · 1 hit paper
13 papers, 333 citations indexed

About

Michael Ko is a scholar working on Oncology, Pulmonary and Respiratory Medicine and Surgery. According to data from OpenAlex, Michael Ko has authored 13 papers receiving a total of 333 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Oncology, 7 papers in Pulmonary and Respiratory Medicine and 3 papers in Surgery. Recurrent topics in Michael Ko's work include Colorectal Cancer Screening and Detection (8 papers), Gastric Cancer Management and Outcomes (7 papers) and Colorectal Cancer Surgical Treatments (4 papers). Michael Ko is often cited by papers focused on Colorectal Cancer Screening and Detection (8 papers), Gastric Cancer Management and Outcomes (7 papers) and Colorectal Cancer Surgical Treatments (4 papers). Michael Ko collaborates with scholars based in Hong Kong, China and United States. Michael Ko's co-authors include Jennifer L. Hicks, Antoine Falisse, Scott D. Uhlrich, Łukasz Kidziński, Scott L. Delp, Akshay Chaudhari, Wai K. Leung, Thomas Ka-Luen Lui, Lung‐Yi Mak and Stephen Tsao and has published in prestigious journals such as SHILAP Revista de lepidopterología, The American Journal of Gastroenterology and BMC Public Health.

In The Last Decade

Michael Ko

12 papers receiving 325 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.

OpenCap: Human movement dynamics from smartphone videos

2023 133 citations Scott D. Uhlrich, Antoine Falisse et al. PLoS Computational Biology profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Michael Ko Hong Kong	9	124	89	63	63	50	13	333
Sara Golla United States	8	43 0.3×	121 1.4×	27 0.4×	23 0.4×	138 2.8×	9	308
Ying-Chun Jheng Taiwan	11	38 0.3×	29 0.3×	42 0.7×	17 0.3×	192 3.8×	23	345
Kenneth P. Seastedt United States	10	23 0.2×	42 0.5×	23 0.4×	81 1.3×	43 0.9×	22	271
Maryam Jessri Australia	12	52 0.4×	28 0.3×	14 0.2×	86 1.4×	53 1.1×	24	493
Lei Cui China	11	43 0.3×	89 1.0×	65 1.0×	50 0.8×	236 4.7×	27	403
Benjamin Schnack Rasmussen Denmark	13	21 0.2×	43 0.5×	41 0.7×	123 2.0×	68 1.4×	46	456
Irfan Ahmed United Kingdom	6	18 0.1×	32 0.4×	22 0.3×	43 0.7×	76 1.5×	16	366
Geoffrey M. Riley United States	11	20 0.2×	68 0.8×	42 0.7×	173 2.7×	65 1.3×	21	411
Youngjune Kim South Korea	9	25 0.2×	33 0.4×	50 0.8×	117 1.9×	62 1.2×	23	271
Liyun Chang Taiwan	11	28 0.2×	153 1.7×	45 0.7×	45 0.7×	150 3.0×	45	379

Countries citing papers authored by Michael Ko

Since Specialization

Citations

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

Fields of papers citing papers by Michael Ko

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michael Ko

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

All Works

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13 of 13 papers shown

Ko, Michael, et al.. (2025). Surveillance findings in high-risk patients after baseline computer-assisted detection colonoscopy: a propensity score matching analysis. Gastrointestinal Endoscopy. 102(3). 384–390.e2.

Lui, Thomas Ka-Luen, Wai‐Pan To, Michael Ko, et al.. (2024). Endocuff With or Without Artificial Intelligence-Assisted Colonoscopy in Detection of Colorectal Adenoma: A Randomized Colonoscopy Trial. The American Journal of Gastroenterology. 119(7). 1318–1325. 14 indexed citations

Uhlrich, Scott D., Antoine Falisse, Łukasz Kidziński, et al.. (2023). OpenCap: Human movement dynamics from smartphone videos. PLoS Computational Biology. 19(10). e1011462–e1011462. 133 indexed citations breakdown →

Leung, Wai K., Vivien Tsui, Lung‐Yi Mak, et al.. (2023). Blue-light imaging or narrow-band imaging for proximal colonic lesions: a prospective randomized tandem colonoscopy study. Gastrointestinal Endoscopy. 98(5). 813–821.e3. 4 indexed citations

Lui, Thomas Ka-Luen, Michael Ko, Jingjia Liu, Xiao Xiao, & Wai K. Leung. (2023). Artificial intelligence–assisted real-time monitoring of effective withdrawal time during colonoscopy: a novel quality marker of colonoscopy. Gastrointestinal Endoscopy. 99(3). 419–427.e6. 5 indexed citations

Lui, Thomas Ka-Luen, Hang Viet Dao, Michael Ko, et al.. (2022). Computer-assisted detection versus conventional colonoscopy for proximal colonic lesions: a multicenter, randomized, tandem-colonoscopy study. Gastrointestinal Endoscopy. 97(2). 325–334.e1. 12 indexed citations

Ko, Michael, Emma Chen, Ashwin Agrawal, et al.. (2021). Improving hospital readmission prediction using individualized utility analysis. Journal of Biomedical Informatics. 119. 103826–103826. 7 indexed citations

Irvin, Jeremy, Michael Ko, Pranav Rajpurkar, et al.. (2020). Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments. BMC Public Health. 20(1). 608–608. 37 indexed citations

Lui, Thomas Ka-Luen, Vivien Tsui, Ka Shing Cheung, et al.. (2020). New insights on missed colonic lesions during colonoscopy through artificial intelligence–assisted real-time detection (with video). Gastrointestinal Endoscopy. 93(1). 193–200.e1. 22 indexed citations

10.

Mak, Lung‐Yi, Michael Ko, Wai‐Pan To, et al.. (2019). Serum Mac‐2‐binding protein glycosylation isomer and risk of hepatocellular carcinoma in entecavir‐treated chronic hepatitis B patients. Journal of Gastroenterology and Hepatology. 34(10). 1817–1823. 14 indexed citations

11.

Lui, Thomas Ka-Luen, Kenneth K. Wong, Lung‐Yi Mak, et al.. (2019). Endoscopic prediction of deeply submucosal invasive carcinoma with use of artificial intelligence. SHILAP Revista de lepidopterología. 7(4). E514–E520. 46 indexed citations

12.

Leung, Wai K., Michael Ko, Wai‐Pan To, et al.. (2019). Linked color imaging versus narrow-band imaging for colorectal polyp detection: a prospective randomized tandem colonoscopy study. Gastrointestinal Endoscopy. 91(1). 104–112.e5. 28 indexed citations

13.

Ko, Michael, Siew C. Ng, Lung‐Yi Mak, et al.. (2016). Infection‐related hospitalizations in the first year after inflammatory bowel disease diagnosis. Journal of Digestive Diseases. 17(9). 610–617. 11 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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