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Topic · Artificial Intelligence

Machine Learning in Healthcare

Machine Learning in Healthcare is a research topic within Artificial Intelligence. Science Explorer counts 24k research works in it since 1950. 9.9% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the application of deep learning techniques in healthcare, particularly in the analysis of electronic health records (EHR). The papers cover a wide range of topics including predictive modeling, patient similarity, disease risk prediction, medical concept embedding, and temporal data analysis. The goal is to leverage deep learning to improve healthcare decision-making and enable precision medicine.

  • Deep Learning
  • Healthcare
  • Electronic Health Records
  • Predictive Modeling
  • Patient Similarity
  • Clinical Event Prediction
  • Disease Risk Prediction
  • Medical Concept Embedding
  • Temporal Data Analysis
  • Precision Medicine
Research works
24k
fractional, since 1950
In the world top 10%
2.3k
per year above
Top-10% rate
9.9%
share of its works in the world top 10%
Growth, 2018-21 → 2022-25
+205%
the tick is no change
Largest producer
United States
30% of the 10 largest, 2022-now
Machine Learning in Healthcare

Which countries lead Machine Learning in Healthcare research?

By volume, the United States and China publish the most (3.5k and 2.8k works in 2022-now).

By volume, 2022-now

  1. 1 United States 3.5k works
  2. 2 China 2.8k works
  3. 3 India 2.8k works
  4. 4 United Kingdom 700 works
  5. 5 Germany 472 works
  6. 6 Canada 360 works
  7. 7 Italy 340 works
  8. 8 South Korea 302 works
  9. 9 Australia 295 works
  10. 10 France 239 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

United States: 29.6%China: 24.0%India: 23.5%United Kingdom: 5.9%6 others listed: 17.0%30%largest
United States3,498 · 29.6%China2,838 · 24.0%India2,784 · 23.5%United Kingdom700 · 5.9%6 others listed2,009 · 17.0%

Shares of the rows listed above, not of the whole node.

Which institutions lead Machine Learning in Healthcare research?

By volume in 2022-now, Harvard University publishes the most Machine Learning in Healthcare research, followed by Design Intelligence (United States) and SRM Institute of Science and Technology.

By volume, 2022-now

  1. 1 Harvard UniversityUnited States 152 works
  2. 2 Design Intelligence (United States)United States 119 works
  3. 3 SRM Institute of Science and TechnologyIndia 94 works
  4. 4 Vellore Institute of Technology UniversityIndia 85 works
  5. 5 Mayo ClinicUnited States 80 works
  6. 6 Stanford UniversityUnited States 75 works
  7. 7 Chandigarh UniversityIndia 71 works
  8. 8 Saveetha UniversityIndia 68 works
  9. 9 Chinese Academy of SciencesChina 67 works
  10. 10 Peking UniversityChina 59 works

Who are the leading researchers in Machine Learning in Healthcare?

The most-cited researchers publishing on Machine Learning in Healthcare include Douglas G. Altman, Christopher J L Murray and Yoshua Bengio.

  1. 1 Douglas G. Altman United Kingdom 862k citations
  2. 2 Christopher J L Murray United States 583k citations
  3. 3 Yoshua Bengio Canada 476k citations
  4. 4 Rob Knight United States 468k citations
  5. 5 Geoffrey E. Hinton Canada 454k citations
  6. 6 Robert John Tibshirani United States 396k citations
  7. 7 Ronald C. Kessler United States 360k citations
  8. 8 John J.V. McMurray United Kingdom 349k citations
  9. 9 Stephan Windecker Switzerland 338k citations
  10. 10 Matthias Egger Switzerland 326k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

What are the most cited papers in Machine Learning in Healthcare?

The most cited paper in Machine Learning in Healthcare is ""Why Should I Trust You?"" (Marco Túlio Ribeiro, Sameer Kumar Singh, Carlos Guestrin, 2016), with 16,496 citations. Next come "Coding Algorithms for Defining Comorbidities in ICD-9-CM and ICD-10 Administrative Data" (10,878) and "Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead" (10,230).

Most cited papers
#PaperReleasedCitations
1 "Why Should I Trust You?" Marco Túlio Ribeiro, Sameer Kumar Singh, Carlos Guestrin Conference paper 8 Aug 2016 16,496
2 Coding Algorithms for Defining Comorbidities in ICD-9-CM and ICD-10 Administrative Data Hude Quan, Vijaya Sundararajan, Patricia Halfon et al. · Medical Care 13 Oct 2005 10,878
3 Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead Cynthia D Rudin · Nature Machine Intelligence Open access 13 May 2019 10,230
4 High-performance medicine: the convergence of human and artificial intelligence Eric J. Topol · Nature Medicine 28 Dec 2018 10,125
5 A Comprehensive Survey on Graph Neural Networks Zonghan Wu, Shirui Pan, Fengwen Chen et al. · IEEE Transactions on Neural Networks and Learning Systems Open access 24 Mar 2020 10,010
All 99 most cited papers, with filters and sorting

Where is Machine Learning in Healthcare research done?

The largest centres of Machine Learning in Healthcare research in 2022-now are Beijing (China), Chennai (India), London (United Kingdom) and Shanghai (China). Among places with at least 20 works in it, it is an unusually large share of all research in Norman, Vijayawada and Greater Noida.

Largest cities, 2022-now

  1. 1 BeijingChina 488 works
  2. 2 ChennaiIndia 327 works
  3. 3 LondonUnited Kingdom 236 works
  4. 4 ShanghaiChina 220 works
  5. 5 New YorkUnited States 178 works
  6. 6 SeoulSouth Korea 162 works
  7. 7 BengaluruIndia 154 works
  8. 8 DhakaBangladesh 145 works
  9. 9 BostonUnited States 137 works
  10. 10 PuneIndia 130 works

Where it is the local speciality

  1. NormanUS · 120.7 works23×
  2. VijayawadaIN · 60.5 works8.5×
  3. Greater NoidaIN · 73.1 works7.3×
  4. PulchowkNP · 23.3 works7.2×
← less than its size predicts1×more →

Location quotient: how much more of its research is in Machine Learning in Healthcare than the world average.

See Machine Learning in Healthcare on the map

Where is the best place to study Machine Learning in Healthcare?

Among universities, judged by research, Harvard University, Stanford University and National University of Singapore score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%10%20%mean 16.66%fractional works in this field (log) →share of work in the world top 10% →Stanford UniversityHarvard UniversityNational University …University of Toronto
above the meannear itbelow it

One dot per university in the first ten of the table below, at the default weights. The upper left is the interesting corner: small places doing unusually strong work.

Excellenceshare of its work in the world top 10%, 2022-now and all time averaged
40%
Specialisationhow much of its work is in this field, counted up to 10× the world share
10%
Sizeits output in this field, on a log scale
30%
Growthoutput growth, counted from -20% to +20%
10%
Internationalshare of its work with a co-author abroad
10%
Each slider sets a weight; the share it gets of the score is beside it.
#UniversityScoreTop 10%SpecialisationWorksGrowth
1Harvard UniversityUnited States 77.416.6%3.5×152 +108.6%
2Stanford UniversityUnited States 71.418.6%3.7×75 +58.6%
3National University of SingaporeSingapore 64.016.4%2.7×44 +118.5%
4University of TorontoCanada 62.214.3%1.7×47 +91.5%
5Yale UniversityUnited States 61.117.3%2.5×38 +104.4%
6Sichuan UniversityChina 60.818.0%1.6×51 +314.1%
7University of California, San FranciscoUnited States 60.418.3%3.3×32 +95.6%
8Vanderbilt UniversityUnited States 60.316.5%3.7×37 +105.7%
9Cornell UniversityUnited States 59.516.1%2.2×40 +82.4%
10University of OxfordUnited Kingdom 59.214.5%2.2×42 +48.2%

Universities only. The score blends excellence (40%), size (30%), specialisation (10%), growth (10%), international reach (10%), 2022-now; growth compares 2018-21 with 2022-25 and counts up to +20%, since a jump from a small base is mostly noise.

Every institution in Machine Learning in Healthcare, country by country

Is Machine Learning in Healthcare research growing?

Output in 2022-2025 was 205% higher than in 2018-2021, peaking in 2025. The fastest-growing topics are Machine Learning in Healthcare.

still counting19801990200020102020
grewheldshrank

The same series as a ribbon: one cell per year, darker for more. The line above answers how much; this answers when.

Its share of all research

Growing output can still be a shrinking share: all of science grows. This is Machine Learning in Healthcare as a percentage of every work published that year, 0.055% in 2024.

still counting0.050%0.10%Machine Learning in Healthcare0.093% in 202519801990200020102020

Fractional works of Machine Learning in Healthcare over all fractional works that year. How the balance of science has shifted.

How much of it reaches the world top 10%

Year by year.

still counting5%10%15%Machine Learning in Healthcare9.9% in 202520102020

Of each year's works in it, the share that reached the world's 10% most cited for their field and year. A field is the world for its own topics, so there is no other line to hold it against.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.

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