A study published in Nature today outlines for the first time how advances in AI can accelerate breakthroughs in infectious disease research and outbreak response.
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 PSI and University of Oxford scientists 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.
Professor Moritz Kraemer, Associate Professor of Computational and Genomic Epidemiology at PSI and lead author of the study, said: ‘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. 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.’
However, not all areas of pandemic preparedness and response will be equally impacted by advances in AI. 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.
Professor Eric Topol, MD, founder and director of the Scripps Research Translational Institute and study author, said: ‘While AI has remarkable transformative potential for pandemic mitigation, it is dependent upon extensive worldwide collaboration and from comprehensive, continuous surveillance data inputs.’
Samir Bhatt from the University of Copenhagen and Imperial College London and study lead author, said: ‘Infectious disease outbreaks remain a constant threat, but AI offers policymakers a powerful new set of tools to guide informed decisions on when and how to intervene.’
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 full paper on the Nature website: https://www.nature.com/articles/s41586-024-08564-w