Posted: 20th February 2025

Associate Professor Seth Flaxman, Research Associate Mengyan Zhang and Professor of Computing Science Nigel Shadbolt are co-authors of a new paper in Nature outlining for the first time how AI can transform the landscape of infectious disease research and improve pandemic preparedness. 

The study – which is published following last week’s AI Action Summit and amidst increasing global debate on AI investment and regulation – puts 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 study is a partnership between scientists from the University of Oxford and colleagues from academia, industry and policy organisations across Africa, America, Asia, Australia and Europe.  

So far, medical applications of AI have predominantly focused on individual patient care, enhancing for example clinical diagnostics, precision medicine, or supporting clinical treatment decisions. This review instead considers the use of AI in population health. The study finds that recent advances in AI methodologies are performing increasingly well even with limited data – a major bottleneck to date. Better performance on noisy and limited data is opening new areas for AI tools to improve health across both high-income and low-income countries.  

Opportunities for AI and pandemic preparedness identified in the research include:  

• Promising 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 

Not all areas of pandemic preparedness and response will be equally impacted by advances in AI, however. For example, whereas protein language models hold great promise for speeding up understanding of how virus mutations can impact disease spread and severity, advances in foundational models might only provide modest improvements over existing approaches to modelling the speed at which a pathogen is spreading.   

The scientists urge caution in suggesting that AI alone will solve infectious disease challenges, but that integration of human feedback into AI modelling workflows might help overcome existing limitations.   

The authors are particularly 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. The authors suggest 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.   

Read the paper in Nature: https://www.nature.com/articles/s41586-024-08564-w