34 hoursth The European Society for Clinical Microbiology and Infectious Diseases (ESCMID) Global (formerly ECCMID) conference in Barcelona, Spain discussed the use of artificial intelligence (AI) to optimize infection prevention and control.
AI can best be described as an umbrella term that describes the use of technology as a tool to help us do things. The field of AI includes machine learning and deep learning. Machine learning is a predictive tool that relies on human intervention to perform data extraction. Conversely, deep learning can take large datasets and perform training and data extraction independently, without human intervention.
In the past five years, there have been approximately 4,000 new publications related to the prevention and control of healthcare-associated infections (HAIs) with AI-based tools. This is a significant increase compared to his 1,350 comparable studies published between 1976 and 2018. Simply put, the age of AI is here. AI can be implemented in surveillance and detection, predictive analytics, antimicrobial stewardship, environmental monitoring, personalized patient care, and education and training. AI has the potential to save lives, improve working conditions for healthcare workers, and make healthcare systems more efficient and cost-effective.
One example of the use of AI in infection prevention and control is the use of facial recognition systems to identify appropriate face mask use within hospitals. Facial recognition systems utilize facial feature extraction systems to determine whether a user is wearing a face mask and whether the mask is worn properly. This system can be implemented as a checkpoint before entering a patient room. Similarly, hand hygiene monitoring systems are being investigated that utilize convolutional neural networks and computer vision to detect bacteria on users' hands. If not satisfied, the system will ask the user to wash their hands according to the World Health Organization's Hand Hygiene Compliance System. The system can track user hand movements to ensure compliance. In a 2022 study, the model was found to have an accuracy of 93.33% for bacteria detection and 85.5% for handwashing compliance.
Another example of the use of AI in infection prevention is the intelligentization of hospital cleaning. The robot is equipped with sensors that can test the environment and air in real time to determine efficient disinfection routes. Furthermore, AI can also be used to investigate hospital-acquired infections. Network graphs can measure patient, laboratory, medical equipment, and healthcare worker interactions to identify possible disease transmission patterns and suggest decontamination actions.
Despite the overwhelming increase in publications on AI 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 requires a multidisciplinary approach. For a doctor to learn and understand the application of AI in medicine he needs to collaborate with AI experts. In addition to a lack of knowledge about AI, there is also a lack of regulation, which is currently a barrier to formal adoption. The future of AI for infection prevention and control holds great promise, but widespread adoption of these technologies is still far away.
ESCMID 2024: Using AI in Infection Prevention and Control was originally created and published by Pharmaceutical Technology, a brand owned by GlobalData.
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