Mark Sendak

49 papers receiving 1.7k citations

Mark Sendak's Hit Papers

Do no harm: a roadmap for responsible machine learning for health care 2019 · 543 citations
5430+2+4Years since publication100200300400500

Peers

Mark Sendak
Comparison fields: 5 of 125
  • Health Informatics 667
  • Health Information Management 214
  • Family Practice 48
  • Artificial Intelligence 454
  • Medical Laboratory Technology 18
Replace Hisham A. Badreldin with:
Hisham A. Badreldin Saudi Arabia
Khalid Bin Saleh Saudi Arabia
Abdulrahman Alshaya Saudi Arabia
Matthieu Komorowski United Kingdom
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Kyu Rhee United States
Shuroug A. Alowais Saudi Arabia
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Citations per field
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Citations per year

Countries citing papers authored by Mark Sendak

Since Specialization
Citations

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

Fields of papers citing papers by Mark Sendak

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Mark Sendak, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Mark Sendak Line = papers co-authored together Mark Sendak links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 54 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Do no harm: a roadmap for responsible machine learning for health care
Hit paper breakdown →
2019543
2 2018140
3 2020139
4 2020114
5 2020104
6 202091
7 202086
8 202072
9 201952
10 202249
11 202042
12 201733
13 202127
14 202425
15 202125
16 202224
17 202223
18 202322
19 202316
20 202216

About Mark Sendak

Mark Sendak is a scholar working on Health Informatics, Artificial Intelligence, Public Health, Environmental and Occupational Health, General Health Professions and Epidemiology, having authored 54 papers that have together received 1.8k indexed citations. Recurring topics across this work include Artificial Intelligence in Healthcare and Education (17 papers), Machine Learning in Healthcare (12 papers), Ethics in Clinical Research (7 papers), Sepsis Diagnosis and Treatment (6 papers), Artificial Intelligence in Healthcare (4 papers), Healthcare cost, quality, practices (4 papers), Health Systems, Economic Evaluations, Quality of Life (3 papers) and Health Policy Implementation Science (2 papers). The work is most often cited by research in Health Informatics (667 citations), Health Information Management (214 citations), Family Practice (48 citations), Artificial Intelligence (454 citations) and Medical Laboratory Technology (18 citations). Mark Sendak has collaborated with scholars based in United States, United Kingdom and Canada. Frequent co-authors include Suresh Balu, Michael Gao, Katherine Heller, Nathan Brajer, Marshall Nichols, Sonoo Thadaney-Israni, Suchi Saria, Pilar N. Ossorio, Finale Doshi‐Velez and David C. Kale. Their work appears in journals such as npj Digital Medicine, Journal of the American Medical Informatics Association, Annals of Emergency Medicine, Healthcare and JAMA Network Open.

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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