Scientists are increasingly using artificial intelligence (AI) and machine learning to study how diseases spread and affect populations. A new analysis of over 7,000 research papers from 2014-2024 shows that AI applications in epidemiology—the study of disease patterns—are growing rapidly. Researchers found that AI is being used to predict disease outbreaks, identify risk factors, and improve public health decisions. The study reveals that while AI shows great promise for understanding health trends and preventing disease, the field is still in early stages with both exciting opportunities and real challenges to overcome.

The Quick Take

  • What they studied: How artificial intelligence and machine learning are being used in epidemiology (the study of disease patterns in populations) and what trends are emerging in this field
  • Who participated: This wasn’t a traditional study with human participants. Instead, researchers analyzed 7,048 published research papers (5,389 in English and 1,659 in Chinese) written between 2014 and 2024 about AI and disease tracking
  • Key finding: The number of research papers about AI in epidemiology is growing significantly each year. The most common uses are making predictions about disease outbreaks, identifying risk factors, and analyzing large health data sets. COVID-19, digital health tools, and health policy are becoming increasingly important focus areas
  • What it means for you: AI is becoming a more important tool for public health officials to predict disease outbreaks and understand health patterns. This could lead to faster disease detection and better prevention strategies in the future, though the technology is still being developed and tested

The Research Details

Researchers conducted what’s called a ‘bibliometric analysis,’ which is a fancy way of saying they studied patterns in published research papers. They searched two major databases of scientific papers—one international (Web of Science) and one focused on Chinese research—for all papers published between 2014 and 2024 that discussed both artificial intelligence and epidemiology. They then used special software called CiteSpace to analyze these papers, looking at things like how many papers were published each year, which topics appeared most frequently, and which papers were cited most often by other researchers. This approach helps identify what scientists are most interested in and where the field is heading.

This type of analysis is important because it gives us a bird’s-eye view of an entire field of research. Instead of reading thousands of individual papers, this study summarizes the big trends and shows us what scientists around the world think are the most important problems to solve. It’s like looking at a map of the entire landscape rather than examining individual trees.

This study is a comprehensive review of published research, which is a reliable way to understand trends in science. The researchers used established methods and analyzed papers from multiple languages and databases, making their findings more complete. However, this study doesn’t test new ideas itself—it summarizes what others have already published. The quality of the conclusions depends on the quality of the papers they analyzed.

What the Results Show

The analysis found that research combining AI and epidemiology is growing rapidly, with more papers published each year. In Chinese research, the most common topics were prediction (forecasting disease patterns), identifying influencing factors (what causes diseases), and machine learning (a type of AI). In English-language research, the top topics were machine learning, prediction, and artificial intelligence in general. The researchers identified 14 major topic clusters in Chinese papers and 21 in English papers, showing the diversity of how AI is being applied to disease study. Key application areas include assessing traffic accident risks, using big data for public health, and using deep learning (advanced AI) for medical diagnosis.

The study found that certain topics are emerging as particularly important in international research: health policy decisions, COVID-19 pandemic response, and digital health tools (like apps and online health monitoring). This suggests that scientists are increasingly interested in how AI can help governments make better health decisions and respond to disease outbreaks. The research also shows that AI is being applied to many different populations, from elderly individuals to working adults, indicating broad interest in personalized health predictions.

This is one of the first comprehensive analyses of how AI is being used in epidemiology as a whole field. While individual studies on AI and disease prediction have existed for years, this research shows that the field has grown significantly and is becoming more organized around specific themes. The emergence of COVID-19 and digital health as major topics reflects recent global events and technological advances that have accelerated AI adoption in public health.

This study only looked at published research papers, so it doesn’t capture unpublished work or real-world applications that haven’t been written up yet. The analysis is based on what researchers chose to publish, which might not represent all the work being done. Additionally, the study is descriptive—it tells us what’s being researched but doesn’t evaluate whether these AI applications actually work well in practice. The researchers also note that the field is still in early stages, so many of these applications are experimental rather than proven solutions.

The Bottom Line

Stay informed about AI developments in public health and disease prevention, as this technology is likely to play an increasing role in how health officials track and respond to disease outbreaks. If you work in public health, healthcare, or epidemiology, consider learning about machine learning and AI tools relevant to your field. For the general public, be aware that AI-based health predictions are becoming more common but are still being developed and should be interpreted carefully. Confidence level: Moderate—the field is promising but still developing.

Public health officials, epidemiologists, healthcare workers, and policymakers should pay attention to these trends as AI tools may help them do their jobs better. Technology companies and software developers interested in health applications should follow this field. The general public should be aware that AI is increasingly involved in disease tracking and health decisions, though most people won’t directly interact with these tools. People should be cautious about AI-based health predictions they encounter online, as the technology is still being refined.

The field is still in early stages, so widespread, proven AI applications in disease tracking may take 5-10 years to become standard practice. Some applications (like COVID-19 prediction models) are already being used, but many others are still being tested and refined. Expect gradual improvements and broader adoption over the next decade.

Want to Apply This Research?

  • Track your exposure to health information sources: note when you encounter AI-based health predictions or recommendations (from apps, websites, or health providers) and monitor how accurate they turn out to be over time. This helps you understand the reliability of AI tools in your own health decisions.
  • If using a health app that includes AI-based predictions (like disease risk assessments or personalized health recommendations), compare those predictions with advice from your doctor or trusted health provider. This helps you understand how AI tools work and whether they’re helpful for your specific situation.
  • Keep a simple log of any AI-based health tools or predictions you use, noting what they predicted and what actually happened. Over several months, you’ll develop a sense of which tools are reliable for your needs. Share this information with your healthcare provider to help them understand which digital tools might be most helpful for you.

This article summarizes a research analysis of published studies about artificial intelligence in epidemiology. It is not medical advice. AI-based health predictions and recommendations are still being developed and should not replace consultation with qualified healthcare professionals. Always discuss any health concerns or decisions with your doctor or healthcare provider. The findings presented represent current research trends and do not guarantee that specific AI applications will be effective for individual health situations. If you are making health decisions based on AI tools or predictions, consult with a medical professional to ensure the information is appropriate for your circumstances.