Artificial Intelligence took the world by a storm in 2024. Come 2025, organizations are increasingly integrating AI into its operations. The healthcare sector is no exception to this. Interestingly, AI is being used to prevent infections in hospitals as the complex nature of HAIs demands an effective IPC system, which AI is now aiding. We spoke to Aaron Woodall, MPH, CIC, LTC-CIP, CM and Bassel Molaeb, Healthcare Advisor and Trainer Healthcare Advisor and Trainer The Compass Health Consultancy to gain more insights about the applicability of AI in infection prevention and control. 

Aaron Woodall, MPH, CIC, LTC-CIP, CM

The integration of Artificial Intelligence (AI) into healthcare has ushered in transformative changes with infection prevention and control (IPC) emerging as a particularly promising application area. The complexity of healthcare-associated infections (HAIs) compounded by evolving microbial resistance patterns and the intricate dynamics of hospital environments, necessitates advanced tools that can offer both predictive insights and real-time interventions. AI, with its capacity to process vast datasets, identify latent patterns, and adapt to emerging challenges is poised to enhance IPC strategies in ways that were previously unattainable. At the heart of AI’s impact on IPC is its ability to analyze heterogeneous data sources ranging from electronic health records (EHRs) and laboratory results to real time sensor data and even social media signals to detect anomalies indicative of potential infection outbreaks. Machine learning algorithms particularly those employing deep learning techniques have demonstrated proficiency in pattern recognition and anomaly detection which can be pivotal for early outbreak detection. For example, predictive and dynamic models can be trained to forecast infection risks based on historical trends, environmental factors, and patient specific vulnerabilities. 1 Such models not only facilitate preemptive interventions but also enable healthcare professionals to allocate resources more effectively, leading to a reduction of the incidence of HAIs and improving patient outcomes.

Refining diagnostic processes

Another critical facet of AI in IPC is its role in automating and refining diagnostic processes. Rapid and accurate identification of pathogens is essential for effective infection control. AI-driven image analysis tools have been applied to microscopy and radiographic data to differentiate between bacterial, viral, and fungal infections with a high degree of accuracy. Natural language processing (NLP) algorithms can mine clinical notes and laboratory reports to extract relevant information that may indicate emerging infection trends. 2 By synthesizing this data AI systems can support clinicians in making evidence-based decisions ultimately leading to more timely and targeted interventions.

Optimizing operational workflows

The deployment of AI in IPC also extends to optimizing operational workflows within healthcare facilities. One of the persistent challenges in IPC is ensuring strict adherence to hygiene protocols and sterilization procedures. AI-powered monitoring systems can provide continuous oversight of hand hygiene compliance, environmental cleaning practices, and the usage of personal protective equipment (PPE). These systems, often integrated with computer vision and sensor technologies offer real-time feedback and can alert staff to lapses in protocol adherence. Proactive monitoring not only mitigates the risk of infection spread but also serves as an educational tool, reinforcing best practices among healthcare workers. AI’s utility in IPC is not confined to routine hospital environments. Its applications are expanding into public health surveillance and outbreak management. During recent global health crises, AI models have been instrumental in predicting the spread of infectious diseases, evaluating the effectiveness of public health interventions, and even assisting in the development of vaccines and therapeutics. 3 These models leverage epidemiological data to simulate various outbreak scenarios, thereby informing policy decisions and enabling health authorities to implement measures that can curb transmission at both local and global scales.

The confluence of AI with geographic information systems (GIS) further enhances the precision of these predictions facilitating targeted interventions in high risk areas.

Challenges and ethical considerations

Despite these promising advancements, several challenges and ethical considerations must be addressed to fully realize the potential of AI in IPC. Data quality and interoperability remain significant hurdles; the efficacy of AI systems is heavily dependent on the availability of accurate, comprehensive, and standardized datasets. Inconsistencies in data entry, prevalence of missing data, and variations in data formats across institutions can compromise model performance. The deployment of AI in clinical settings raises important questions regarding data privacy, informed consent, and the potential for algorithmic bias. Ensuring that AI systems are transparent, explainable, and subject to rigorous validation protocols is essential to maintaining trust among healthcare professionals and patients alike. 4 Another critical consideration is the integration of AI tools into existing clinical workflows. Successful implementation requires a collaborative approach that involves clinicians, data scientists, and healthcare administrators. User friendly interfaces, comprehensive training programs, and ongoing technical support are necessary to bridge the gap between AI development and practical application. 5 Regulatory frameworks must evolve to keep pace with technological advancements to ensure that AI systems in IPC are subject to stringent oversight and continuous quality improvement measures.

AI presents a transformative opportunity for IPC, offering capabilities that extend from predictive analytics and diagnostic support to operational optimization and public health surveillance. By harnessing the power of AI healthcare systems can enhance their ability to detect, prevent, and manage infections, ultimately leading to improved patient safety and outcomes. Though realizing this potential requires addressing critical challenges related to data quality, ethical considerations, and system integration. As the field continues to evolve, interdisciplinary collaboration and proactive policy development will be crucial in ensuring that AI driven innovations translate into tangible benefits for both healthcare providers and the populations they serve.

Bassel Molaeb, Healthcare Advisor and Trainer Healthcare Advisor and Trainer The Compass Health Consultancy

Healthcare environments are inherently complex, with high patient turnover, invasive procedures, and the constant presence of pathogens. Traditional infection control measures, such as hand hygiene compliance, surface disinfection, and sterilization protocols, rely heavily on human adherence and manual monitoring. However, human error, inconsistent practices, and limited real-time data often hinder the effectiveness of these measures. According to the World Health Organization, approximately 7% of hospitalized patients in developed countries and 10% in developing countries acquire at least one healthcare-associated infection (HAI), underscoring the need for more robust and proactive solutions.

Using AI in Healthcare Hygiene

Artificial intelligence (AI) is transforming infection prevention and control (IPC) in healthcare by enhancing surveillance, improving hygiene compliance, and optimizing antimicrobial stewardship. With increasing HAIs and the growing threat of antimicrobial resistance, AI-driven solutions offer a promising approach to strengthen hygiene protocols and improve patient safety. Below are some key applications of AI in healthcare hygiene:

AI in Hand Hygiene and Personal Protective Equipment (PPE) Monitoring and Training

Hand hygiene and proper PPE use are critical for infection prevention, yet compliance remains suboptimal in many healthcare settings. AI-powered systems are addressing these challenges through real-time monitoring and training. Using computer vision and smart sensors, AI tracks hand hygiene events and PPE application, providing instant feedback to healthcare workers and generating compliance reports. Studies show AI-based monitoring significantly improves adherence and reduces HAIs. Innovative tools like Blue Mirror 3 , an AI training system, turn devices into digital mirrors that assess and correct hand hygiene and PPE errors in real time. This saves time and resources while improving infection control by minimizing errors. By integrating AI for monitoring and training, healthcare facilities can enhance compliance, reduce infections, and create a culture of continuous improvement in hygiene practices. 

Automated Environmental Hygiene Assessment

Hospital environments are key reservoirs for pathogens, making AI-driven disinfection essential in infection control. Robotic systems with AI and UV-C technology now autonomously disinfect hospital rooms, targeting high-touch surfaces with precision. AI-powered contamination detection analyzes swabs and air samples to identify hotspots, enabling timely interventions. Smart sensors monitor airborne pathogens, triggering real-time alerts for corrective actions. A notable example is the Shyld AI 4 device, which automates ultraviolet (UV) disinfection without manual intervention, ensuring rapid room turnover in high-traffic patient areas. These innovations significantly enhance environmental hygiene and reduce infection risks.

AI in Predicting and Preventing Outbreaks

AI algorithms can analyze vast amounts of data, including patient records, environmental factors, and microbial surveillance data, to predict and prevent potential infection outbreaks. For example, machine learning models can identify patterns and risk factors associated with HAIs, allowing healthcare facilities to implement preemptive measures. This predictive capability is particularly valuable in managing multidrug-resistant organisms (MDROs) and other high-risk pathogens.

Enhancing Antimicrobial Stewardship

Antimicrobial resistance (AMR) is a significant global health challenge, necessitating robust antimicrobial stewardship programs. AI-driven clinical decision support systems assist clinicians in optimizing antibiotic prescribing practices by analyzing vast datasets, including microbial resistance patterns and patient history. These systems provide evidence-based recommendations, reducing unnecessary antibiotic use and minimizing the risk of resistant infections. Additionally, AI-powered natural language processing tools can analyze clinical notes to detect inappropriate antibiotic prescriptions, reinforcing adherence to antimicrobial stewardship guidelines.

The Future of AI in Healthcare Hygiene

AI is revolutionizing infection control by automating tasks, providing real-time insights, and enabling predictive capabilities. However, successful implementation requires addressing ethical, privacy, and regulatory concerns while fostering collaboration between healthcare professionals and technology developers.

AI-driven innovations enhance hand hygiene, predict outbreaks, and improve antimicrobial stewardship, reducing HAIs and boosting patient safety. As these technologies evolve, their impact on infection prevention will grow, creating safer healthcare environments. Facilities that embrace AI can streamline processes, strengthen workforce competency, and proactively reduce infection risks.

To summarise, the integration of Artificial Intelligence (AI) in infection prevention and control (IPC) is revolutionizing healthcare, offering predictive insights, real-time monitoring, and automated interventions that enhance patient safety. As we step into 2025, AI-driven solutions are proving invaluable in reducing healthcare-associated infections (HAIs), optimizing hygiene compliance, and improving antimicrobial stewardship.

Experts Aaron Woodall and Bassel Molaeb emphasize that AI is not just a tool but a game-changer in IPC. From AI-powered hand hygiene monitoring systems and automated environmental disinfection to machine learning models predicting infection outbreaks, the role of AI in healthcare is expanding rapidly. These advancements minimize human error, streamline hospital operations, and ensure higher adherence to hygiene protocols—all crucial factors in reducing the spread of infections.

However, the adoption of AI in IPC comes with challenges. Issues such as data quality, ethical considerations, regulatory compliance, and the need for seamless system integration must be addressed for AI to reach its full potential. Healthcare institutions must also invest in training to ensure that frontline workers can effectively use these AI-powered tools.

The future of AI in healthcare hygiene looks promising. As hospitals, clinics, and healthcare providers continue to embrace AI, infection prevention strategies will become more proactive, data-driven, and efficient. With ongoing advancements, AI is set to play a pivotal role in shaping a safer, smarter, and more resilient healthcare system for years to come.