AJMB2501 - CME/CMLE - Potential roles for artificial intelligence in clinical microbiology from improved diagnostic accuracy to solving the staffing crisis-AJMB2501

AJMB2501 - Educational Activity

Pdf Summary

This review article focuses on the transformative potential of artificial intelligence (AI) in clinical microbiology, highlighting its capacity to enhance diagnostic accuracy and efficiency while addressing staffing shortages in the field. The authors, led by Preeti Pancholi, explore the application of AI technologies, particularly machine learning (ML) and convolutional neural networks (CNNs), in automating labor-intensive tasks. These tasks include interpreting Gram stains, reading culture plates, and conducting antimicrobial susceptibility testing.<br /><br />Artificial intelligence shows promise in automating microscopic slide analysis, such as identifying and classifying microbes in blood cultures, respiratory specimens, and stool samples, with studies showing AI performance often surpassing that of human experts. For example, AI systems have demonstrated notable accuracy in automating Gram stain interpretation and detecting difficult-to-diagnose conditions like bacterial vaginosis.<br /><br />Macroscopic agar plate analysis also benefits from AI applications. AI can accurately differentiate between organism growth on chromogenic agars and automate colony counting on urine cultures. Additionally, AI can assist in antimicrobial susceptibility testing by reading disk diffusion zones more swiftly and accurately than traditional methods, potentially providing faster and more reliable results for bacterial resistance profiling.<br /><br />The authors emphasize the importance of addressing training data biases and overfitting to improve AI's accuracy and reliability. However, despite promising results, none of the discussed AI solutions have received U.S. Food and Drug Administration (FDA) clearance. They stress the need for standardized and FDA-approved AI systems to ensure broader applicability and facilitate their integration into routine clinical workflows. In conclusion, AI advancements in clinical microbiology offer a promising avenue for automating processes, but regulatory and implementation hurdles remain to be addressed for widespread adoption.

Keywords

artificial intelligence

clinical microbiology

machine learning

convolutional neural networks

diagnostic accuracy

antimicrobial susceptibility testing

Gram stain interpretation

FDA clearance

training data biases

automating processes