A Bibliometric Analysis of Artificial Intelligence Applications in Infectious Diseases: A Decade of Research (2016–2025)
This bibliometric analysis of 1,252 articles from 2016 to 2025 reveals an exponential growth in research on artificial intelligence applications for infectious diseases, driven largely by the COVID-19 pandemic and increasingly focused on clinical hotspots such as antimicrobial resistance surveillance and sepsis prediction.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
Imagine the world of infectious diseases (like the flu, tuberculosis, or sepsis) as a massive, chaotic library. For years, doctors and scientists have been trying to find the right books to cure patients, but the library is getting bigger every day, and the books are written in complex languages.
This paper is like a mapmaker who decided to take a decade-long tour of this library (from 2016 to 2025) to see how a new tool—Artificial Intelligence (AI)—has been changing the way we search for answers. Instead of reading every single book, the authors used a "counting machine" (bibliometric analysis) to look at 1,252 specific research papers to understand the big picture.
Here is what they found, explained simply:
1. The Explosion of Interest
For the first few years of the tour (2016–2018), the AI section of the library was quiet. But then, something happened: the COVID-19 pandemic. It was like a sudden storm that blew the doors off the library.
- The Growth: Before the storm, there were very few papers. By 2025, the number of papers had grown 30 times larger than it was in 2016.
- The Rush: Almost 85% of all the research in this entire decade happened in just the last five years. It's as if everyone suddenly realized, "We need to use AI to fight these diseases, and we need to do it now."
2. Who is Doing the Work?
If this research were a global relay race, two runners are clearly in the lead:
- The United States and China: These two countries are running the fastest. They wrote the most papers and are the ones holding hands with the most other countries to share ideas.
- The Top Schools: Harvard University is the team with the most runners, but Johns Hopkins University has the runners who are getting the most attention (citations) for their work.
- The Surprise Star: While India didn't write the most papers, their papers got the most attention per paper. It's like they wrote fewer books, but the ones they wrote were bestsellers.
3. The Nine "Neighborhoods" of Research
The authors used a special lens to group the research into nine distinct "neighborhoods" or clusters. Imagine these as different rooms in the library where people are working on specific problems:
- The "Early Warning" Room: This is the biggest room. People here are using AI to listen to social media and big data to spot disease outbreaks before they happen, kind of like a weather forecast for viruses.
- The "Superbugs" Room: This is a very hot area right now. Scientists are using AI to figure out how bacteria are becoming resistant to antibiotics (like when a lock changes shape and the key no longer fits). They are trying to find new ways to pick those locks.
- The "Sepsis" Room: Sepsis is a life-threatening reaction to infection. Researchers are building AI "guard dogs" that can sniff out sepsis in patients hours before a human doctor might notice, potentially saving lives.
- The "Pandemic" Room: This room was built during the COVID-19 crisis. It's full of models trying to predict how fast a virus will spread, like tracking a fire through a forest.
- The "Classic Diseases" Room: This room has steady work on HIV, Tuberculosis, and Malaria. These aren't new problems, but AI is helping to diagnose them faster, especially in places where doctors are scarce.
- The "X-Ray" Room: Here, computers are learning to look at chest X-rays to find Tuberculosis, acting like a super-radiologist that never gets tired.
- The "Explainable" Room: A newer area where scientists are trying to make AI "speak human." Instead of the AI just giving an answer, they want it to explain why it made that decision, so doctors can trust it.
4. What's Next?
The map shows that the field is moving fast. The biggest trends right now are:
- Fighting Superbugs: Finding better ways to handle antibiotic resistance.
- Saving Lives in the ICU: Using AI to predict sepsis early.
The Bottom Line
This paper doesn't tell us that AI has already cured everyone. Instead, it shows us that the foundation is being built. The world has realized that AI is a powerful tool for infectious diseases. The map shows us where the builders are working, who is leading the construction crew, and which rooms (like sepsis and antibiotic resistance) are getting the most attention.
The authors conclude that while there is still work to do, AI is no longer just a science experiment; it is becoming a permanent, essential part of the toolkit doctors will use to fight infections in the future.
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