Scientists from the University of Oxford (UK) outline how Artificial Intelligence (AI) can transform the landscape of infectious disease research and improve pandemic preparedness. For the scientists, this is essential for preparing for the next global disease after COVID-19.
The call comes in the form of a research study, published following the AI Action Summit in Parise (February 2025). This comes amidst increasing global debate on AI investment and regulation. The study places particular emphasis on safety, accountability and ethics in the deployment and use of AI in infectious disease research.
Calling for a collaborative and transparent environment — both in terms of datasets and AI models — the researchers are seeking a global framework, one that is acceptable to the majority of nations.
To date, medical applications of AI have predominantly focused on individual patient care. Applications include enhancing clinical diagnostics, precision medicine, or supporting clinical treatment decisions.
The review takes a different approach, considering the use of AI in population health. The outcome is that recent advances in AI methodologies are performing increasingly well even with limited data. From this there is the potential for AI tools to improve health across both high-income and low-income countries, as data quality gets better.
Lead author Professor Moritz Kraemer from the University of Oxford’s Pandemic Sciences Institute, states: “In the next five years, AI has the potential to transform pandemic preparedness. It will help us better anticipate where outbreaks will start and predict their trajectory, using terabytes of routinely collected climatic and socio-economic data. It might also help predict the impact of disease outbreaks on individual patients by studying the interactions between the immune system and emerging pathogens.”
Kraemer adds: “Taken together and if integrated into countries’ pandemic response systems, these advances will have the potential to save lives and ensure the world is better prepared for future pandemic threats.”
As to what this means in practice, examples include:
- Advances in improving current models of disease spread, aiming to make modelling more robust, accurate and realistic.
- Progress in pinpointing areas of high-transmission potential, helping ensure limited healthcare resources can be allocated in the most efficient possible way.
- Potential to improve genetic data in disease surveillance, ultimately accelerating vaccine development and the identification of new variants.
- Potential to help determine the properties of new pathogens, predict their traits and identify whether cross species jumps are likely.
- Predicting which new variants of already-circulating pathogens — such as SARS-CoV-2 and influenza viruses — might arise, and which treatments and vaccines are best in reducing their impact.
- Possible AI-aided integration of population-level data with data from individual-level sources — including wearable technologies such as heart rate and step counts — to better detect and monitor outbreaks.
- AI can create a new interface between the highly technical science and healthcare professionals with limited training, improving capacity in settings that need these tools the most.
Limitations of AI
However, AI is not at the stage where it can answer every aspect of medicine. The scientists urge caution in suggesting that AI alone will solve infectious disease challenges. Where AI can assist is with the integration of human feedback into AI modelling workflows might help overcome existing limitations.
The scientists are currently concerned with the quality and representativeness of training data, the limited accessibility of AI models to the wider community, and potential risks associated with the deployment of black-box models for decision making.
There are also ethical considerations. The researchers call for rigorous benchmarks to evaluate AI models, advocating for strong collaborations between government, society, industry and academia for sustainable and practical development of models for improving human health.
The study appears in the journal Nature, titled “Artificial intelligence for modelling infectious disease epidemics.”
