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. 25.0% 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%
- 5.9k per year above
- Top-10% rate
- 25.0% share of its works in the world top 10%
- Growth, 2013–17 → 2018–22
- +283% the tick is no change
Which countries lead Machine Learning in Healthcare research?
By volume, the United States and India publish the most (2.7k and 2k works in 2022–2025).
By volume, 2022–2025
- 1 United States 2.7k works
- 2 India 2k works
- 3 China 1.9k works
- 4 United Kingdom 550 works
- 5 Germany 376 works
- 6 Canada 286 works
- 7 Italy 270 works
- 8 Australia 235 works
- 9 South Korea 231 works
- 10 France 186 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.
Shares of the rows listed above, not of the whole node.
Which institutions lead Machine Learning in Healthcare research?
By volume in 2022–2025, Design Intelligence (United States) publishes the most Machine Learning in Healthcare research, followed by Vellore Institute of Technology University and Chandigarh University.
By volume, 2022–2025
- 1 Design Intelligence (United States)United States 119 works
- 2 Vellore Institute of Technology UniversityIndia 63 works
- 3 Chandigarh UniversityIndia 62 works
- 4 SRM Institute of Science and TechnologyIndia 52 works
- 5 Saveetha UniversityIndia 50 works
- 6 Harvard UniversityUnited States 48 works
- 7 Stanford UniversityUnited States 45 works
- 8 Amrita Vishwa VidyapeethamIndia 36 works
- 9 University of North Carolina at Chapel HillUnited States 36 works
- 10 Peking UniversityChina 33 works
Who are the leading researchers in Machine Learning in Healthcare?
The most-cited researchers publishing on Machine Learning in Healthcare include Philip S. Yu.
- 1 Philip S. Yu United States 6.6k citations
Ranked by citations received across their whole record, among researchers with at least three works on this topic.
Where is Machine Learning in Healthcare research done?
The largest centres of Machine Learning in Healthcare research in 2022–2025 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, Mohali and Vijayawada.
Largest cities, 2022–2025
- 1 Beijing China 345 works
- 2 Chennai India 232 works
- 3 London United Kingdom 186 works
- 4 Shanghai China 161 works
- 5 New York United States 141 works
- 6 Seoul South Korea 126 works
- 7 Norman United States 120 works
- 8 Bengaluru India 115 works
- 9 Boston United States 110 works
- 10 Dhaka Bangladesh 109 works
Where it is the local speciality
- NormanUS · 120.5 works30×
- MohaliIN · 65.0 works8.4×
- VijayawadaIN · 42.9 works8.0×
- Greater NoidaIN · 54.8 works7.5×
Location quotient: how much more of its research is in Machine Learning in Healthcare than the world average.
Where is the best place to study Machine Learning in Healthcare?
Among universities, judged by research, Harvard University, Chandigarh University and Stanford University 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.
One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.
| # | University | Score | Top 10% | Specialisation | Works | Growth |
|---|---|---|---|---|---|---|
| 1 | Harvard UniversityUnited States | 57.5 | 34.6% | 3.4× | 48 | +231.5% |
| 2 | Chandigarh UniversityIndia | 57.4 | 20.7% | 9.7× | 62 | — |
| 3 | Stanford UniversityUnited States | 55.2 | 34.6% | 3.2× | 45 | +439.6% |
| 4 | Princess Nourah bint Abdulrahman UniversitySaudi Arabia | 53.8 | 62.5% | 3.0× | 9 | — |
| 5 | Daffodil International UniversityBangladesh | 52.0 | 29.1% | 9.7× | 10 | — |
| 6 | Imperial College LondonUnited Kingdom | 51.8 | 31.8% | 2.7× | 28 | +253.6% |
| 7 | The University of Texas Health Science Center at HoustonUnited States | 51.4 | 31.1% | 5.4× | 18 | +376.1% |
| 8 | University of OxfordUnited Kingdom | 51.1 | 35.4% | 2.0× | 29 | +337.2% |
| 9 | Icahn School of Medicine at Mount SinaiUnited States | 50.6 | 26.9% | 4.6× | 26 | +468.4% |
| 10 | Bennett UniversityIndia | 50.6 | 25.2% | 15.4× | 17 | — |
Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.
Is Machine Learning in Healthcare research growing?
Output in 2018–2022 was 283% higher than in 2013–2017, peaking in 2025. The fastest-growing topics are Machine Learning in Healthcare.
The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.
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.