AI in infectious disease control and AMR: Promise and challenges

Artificial intelligence (AI) is rapidly transforming healthcare, and its impact on infectious disease prevention, diagnosis, and treatment is drawing growing attention. A new series in The Lancet Infectious Diseases presents a comprehensive look at how AI can be harnessed to manage infectious diseases and combat antimicrobial resistance (AMR), highlighting both the potential and the barriers to widespread adoption.

AI technologies are proving valuable across multiple aspects of infectious disease management. These include outbreak detection, real-time disease surveillance, infection control, clinical diagnostics, and antimicrobial stewardship. Leveraging data from pathogens, human hosts, and environmental sources, AI can detect subtle patterns and trends that are often beyond the capacity of traditional methods. The series proposes a conceptual framework that identifies key domains where AI can be integrated across research, public health, and clinical care.

One of the most promising areas of AI application is in diagnostics. By supporting clinical decision-making, optimising laboratory workflows, and enhancing the speed and accuracy of pathogen detection, AI is reshaping the way infectious diseases are diagnosed and monitored. This is especially critical for managing antimicrobial resistance. AI can assist in identifying resistant pathogens quickly and suggest personalised treatments, contributing to more targeted and effective use of antibiotics — a cornerstone of antimicrobial stewardship.

Despite the potential, challenges remain significant. High-income countries, while more technologically advanced, struggle with fragmented health data systems, algorithmic biases, and difficulties in integrating AI into clinical practice. On the other hand, low- and middle-income countries face even more fundamental barriers, such as lack of digital infrastructure, standardised data, and financial resources. These disparities risk widening the existing global health inequities if not addressed through targeted policy and investment.

In the context of antimicrobial resistance, AI could play a critical role in achieving the recently defined UN General Assembly targets, which call for a global, multisectoral approach to address AMR. AI's ability to process and analyse vast amounts of clinical and microbiological data can improve antimicrobial surveillance, support the development of new antibiotics, and enhance public health responses. Moreover, AI tools can provide early warning systems for emerging resistance patterns.

However, for AI to reach its full potential in infectious disease management and AMR, several systemic issues must be resolved. These include ensuring data interoperability across systems, protecting patient privacy, managing cybersecurity risks, and navigating complex regulatory environments. Ethical concerns, such as ensuring algorithmic fairness and transparency, are also key considerations.

The Lancet Series underscores the need for coordinated global efforts to overcome these challenges. Investments in digital infrastructure, particularly in lower-resource settings, will be essential. Equally important is the development of harmonised data-sharing policies and training for healthcare professionals to effectively use AI-driven tools.