At 34Th The European Society of Clinical Microbiology and Infectious Diseases (ESCMID) global (formerly ECCMID) conference took place in Barcelona, Spain, to discuss the use of artificial intelligence (AI) to improve infection prevention and control. went.
AI can best be described as a term for using technology as a tool to help us do something. In the field of AI, there is machine learning and deep learning. Machine learning is a predictive tool that relies on human intervention to extract data. In contrast, deep learning is able to take large data sets and perform learning and data extraction independently, without human intervention.
Over the past five years, there have been nearly 4,000 new publications on the prevention and control of healthcare-associated infections (HAIs) through AI-based tools. This is a significant increase from the 1,350 comparative studies published between 1976-2018. Simply put, the age of AI is here to stay. AI can be applied in surveillance and detection, predictive analytics, antimicrobial stewardship, environmental monitoring, personalized patient care, and education. AI has the potential to save lives, improve working conditions for healthcare professionals, and make healthcare systems more efficient and cost-effective.
An example of the use of AI in infection prevention and control is the use of facial recognition systems to identify the appropriate use of face masks in hospitals. A facial recognition system uses a facial feature extraction system to determine whether or not the user is wearing a face mask, and whether the mask is being worn correctly. This system can be implemented as a checkpoint before the patient enters the room. Similarly, a hand hygiene monitoring system that uses convolutional neural networks and computer vision to detect germs on a user’s hands has been studied. If unsatisfactory, the system will request that the user wash their hands following the World Health Organization hand hygiene compliance system. The system can track the user’s hand movements to ensure compliance. In a 2022 study, the model was found to be 93.33% accurate for germline detection and 85.5% accurate for handwashing compliance.
Another example of the use of AI in infection prevention is the intelligent cleaning of hospitals, where robots are equipped with sensors that can analyze the environment and air in real time to determine the effective course of disinfection. . Additionally, AI can be used to investigate hospital outbreaks. Network graphs can measure interactions between patients, exam rooms, medical devices, and healthcare workers to identify potential patterns of disease transmission and suggest decontamination measures. can
Despite the plethora of AI publications in recent years, AI in infection prevention and control is still in its infancy. Healthcare providers agree that successful implementation of AI in infection prevention and control will require a multidisciplinary approach. Doctors need collaboration with AI experts to help them learn and understand the applications of AI in healthcare. In addition to the lack of knowledge on AI, there is also a lack of regulation, which is the current barrier to any official implementation. The future of AI for infection prevention and control holds great potential, but mass adoption of these technologies is still far off.
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