In today’s digital era, data is generated at an unprecedented scale. Healthcare is no exception— data is produced continuously as a result of our interactions with healthcare organisations - community, acute and tertiary alike. The challenge for healthcare institutions and their governance systems is to utilise this rich healthcare data   effectively and efficiently to improve patient outcomes. Towards this objective, AI is emerging as a key enabling tool. Infection prevention and control (IPC) units have varied work streams - infection surveillance, patient pathway monitoring, novel pathogen intelligence, policy and guidance directives and are best poised to take a leading role in utilising healthcare data to increase the impact of these activities.

Understanding AI: More Than Just a Buzzword

Artificial Intelligence (AI) is an umbrella term encompassing various technologies, including machine learning and deep learning. These technologies power algorithms that continuously evolve and improve based on data. We see AI’s learning capabilities in action regularly—at a lay population level virtual assistants like Alexa refine recommendations daily based on user preferences, and at a policy level, pandemic prediction models help governments anticipate infection surges.

IPC is poised to embrace the transformative potential of AI, ensuring its services evolve in step with technological advances.   Achieving this will require a multi-pronged approach to support the parallel development of both AI capabilities and IPC services.

• Supporting IPC practitioners’ understanding of AI’s potential – e.g. utilising AI’s ability to analyse patient data to produce hospital infection hotspot charts, overlayed with antibiotic usage charts to help understand the correlation between antibiotic use and infection incidence.
• Evaluating clinical efficacy - supporting clinical workflows within hospitals and community healthcare settings – e.g. allowing AI to interrogate patient pathway data to find the most efficient clinical pathways through the hospital, delivering better care, reducing waiting times and promoting timely discharges.
• Training healthcare professionals to harness AI as a tool, rather than viewing it as a replacement for human expertise, by adopting the ‘Centaur’ model—a concept where AI serves as an intelligent assistant rather than an independent decision-maker. For example,  AI can interrogate patient antibiotic treatment regimes and outcomes to aid simple adjustments to advice on treatment   to account for multi drug-resistant organism led infections.
• Implementing governance frameworks to safeguard patient data, ensuring confidentiality, anonymisation, and ethical AI deployment.
• Enhancing data operability and system homogeneity to optimise AI-driven insights and improve patient safety, allowing for AI led clinical decision support systems to play a crucial role in public health and disease management including:

1. Epidemiology and disease surveillance: AI predicts infection hotspots, aiding public health response strategies.
2. Infection transmission modelling: AI refines our understanding of disease spread within hospital and community settings.
3. Personalised treatment recommendations: AI supports antimicrobial stewardship, guiding physicians in selecting optimal therapies based on patient-specific data and past clinical outcomes.

AI’s Role in Workforce Development

AI is not just a clinical tool - it can revolutionise healthcare education  as well. Intelligent learning platforms have the ability to tailor educational resources to enhance the learner’s understanding of AI, data governance, and ethical considerations, fostering an AI-conscious healthcare workforce.

Current Challenges: Effectively and responsibly using the power of AI requires an understanding of the ethical and legal challenges involved.

• Patient privacy and confidentiality – ensuring a strong governance and data protection framework to safeguard patient data
• Bias and fairness – utilising broad patient data sets which are tested for representativeness to mitigate against propagating bias and promote fairness in healthcare delivery
• Transparency and accountability – building clinical decision systems which allow IPC to interrogate how and why AI led systems are suggesting recommendations
• Liability and malpractice – ensuring there are clear lines of accountability with the healthcare professional the final arbiter and deliverer of clinical advice.

The Road Ahead

AI in healthcare, especially in IPC units is not a distant vision—it is happening now. By leveraging AI responsibly, IPC units around the country can enhance patient outcomes, streamline operations, and ensure ethical AI integration. The future is not about AI replacing human expertise but rather about AI empowering professionals to make better, data-driven decisions.

Authored by Sid Mookerjee, an Assistant Professor in Global Health and Infection, Brighton and Sussex Medical School (BSMS) and is part of the organising committee for HISCON 2025
LinkedIn : http://www.linkedin.com/in/sid-mookerjee
BSMS: https://profiles.sussex.ac.uk/p657696-sid-mookerjee

Interested in exploring this topic further? Attend our annual conference, HISCON, where AI’s growing role in IPC and healthcare will be explored in greater depth. More information including full programme can be found in the link below.