Using Artificial Intelligence in Infection Prevention

Abstract

Purpose of Review Artificial intelligence (AI) offers huge potential in infection prevention and control (IPC). We explore its potential IPC benefits in epidemiology, laboratory infection diagnosis, and hand hygiene. Recent Findings AI has the potential to detect transmission events during outbreaks or predict high-risk patients, enabling development of tailored IPC interventions. AI offers opportunities to enhance diagnostics with objective pattern recognition, standardize the diagnosis of infections with IPC implications, and facilitate the dissemination of IPC expertise. AI hand hygiene applications can deliver behavior change, though it requires further evaluation in different clinical settings. However, staff can become dependent on automatic reminders, and performance returns to baseline if feedback is removed. Summary Advantages for IPC include speed, consistency, and capability of handling infinitely large datasets. However, many challenges remain; improving the availability of high-quality representative datasets and consideration of biases within preexisting databases are important challenges for future developments. AI in itself will not improve IPC; this requires culture and behavior change. Most studies to date assess performance retrospectively so there is a need for prospective evaluation in the real-life, often chaotic, clinical setting. Close collaboration with IPC experts to interpret outputs and ensure clinical relevance is essential.

Expert commentary on the challenges and opportunities for surgical site infection prevention through implementation of evidence-based guidelines in the Asia–Pacific Region

“…In the future, innovations such as semi-automated or automated surveillance systems, use of vaccines and molecular therapies, implant/suture product developments, and use of dispersion signals and matrix degraders may be employed to reduce SSI risk [86][87][88][89][90][91]. It is also anticipated that artificial intelligence will be increasingly used to capture surveillance data and will become an integral part of transformative innovation in the future prevention of SSI [92]. Finally, as we have seen during the current pandemic period, telehealth technologies hold a promising future for the implementation and compliance of periand postoperative surgical care-processes [93][94][95].…”

COVID-19 prediction analysis using artificial intelligence procedures and GIS spatial analyst: a case study for Iraq

“…Based on different artificial intelligent neural networks’ (ANNs) approaches, many researchers have been investigating medical data to predict several scenarios related to infectious diseases like predicting the rapid spread and contaminated area (Steiner et al 2020 ; Naudé 2020 ). In the field of combating clinical diseases or highly infectious diseases such as COVID-19 that caused by small organisms such as bacteria or viruses, and due to the accessibility of relevant clinical data, AIT has mostly played a vital role in medical decision-making (Peiffer-Smadja et al 2019 ; Fitzpatrick et al 2020 ; Stebbing et al 2020 ; Yang et al 2020 ). Therefore, to address this epidemic problem early, suitable applications of artificial intelligent forecasting procedures were established as crucial plans to facing this virus.…”

COVID-19: Advances in diagnostic tools, treatment strategies, and vaccine development

“…ML-based solutions can be expected to consistently enhance and refine such designs over time, provided they are trained on a readily updated and expertly-curated dataset. This represents an important caveat, as the efficacy and accuracy of ML-based pattern prediction and recognition may be maintained only if the training dataset is free of biases and a diverse range of scenarios are well-represented (Fitzpatrick et al 2020 ). We had previously referred to the work by Apostolopoulos and Bessiana, which could screen for SARS-CoV-2 infections using X-ray images.…”