Automated Whole Slide Scanning and Artificial Intelligence-assisted Image Analysis for Clinical Microbiology Diagnostics: A Narrative Review

revolution. 1 Digitalization helps in creating whole slide images (WSIs) from microscopic glass slides and enables highthroughput analysis. 4This transition provides a wide array of benefits, which has been discussed in the following sections.WSIs can be easily stored, shared, and analyzed using computer software, overcoming limitations regarding storage space and facilitating collaboration among experts over wide geographic distance. 5Initiatives focusing on development of low-cost WSI acquisition systems 6,7 and workflows that can adapt to low-quality input 4 will ensure equitable access to advanced diagnostic technologies. IntroductIonThe digital technologies are rapidly advancing, particularly in imaging and artificial intelligence (AI).This is revolutionizing various fields, including clinical microbiology and infectious disease diagnostics.Automated whole slide (AWS) scanning and AI-assisted image analysis represent an important progression happening in this field that improves the speed, accuracy, and efficiency of diagnosing infectious diseases. 1,2Timely identification of pathogens, antimicrobial resistance detection, disease surveillance, and monitoring of outbreaks are some important areas in infectious disease management that challenge healthcare professionals and other stakeholders.These technological advancements can help in addressing these challenges effectively. 3Automated systems can analyze large volumes of slide images under short time and can provide consistent results by using machine learning and deep learning algorithms.This can reduce human error and improve turnaround times for diagnoses.Additionally, the effective utilization of these technologies could facilitate remote consultations and collaborations among healthcare professionals, leading to more precise and timelier patient care, especially in infectious disease management.This review examines the current state of AWS and AI-assisted image analysis in clinical microbiology, reviewing its current status, applications, advantages, challenges, and future prospects. the transItIon to dIgItal MIcrobIologyThe transition happening from traditional microscopy toward digital microbiology forms the basis of this technological