THE APPLICATION OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN MEDICAL LABORATORY DIAGNOSTICS.

Abstract

The use of machine learning (ML) algorithms in clinical laboratory medicine has transformed a number of areas related to patient care and diagnosis. An overview of machine learning applications in clinical laboratories is given in this work, with particular attention on automated interpretation, predictive modeling, error detection, and clinical decision support systems. Machine learning (ML) systems, specifically in the context of supervised learning, have demonstrated exceptional precision in forecasting disease outcomes, identifying errors during pre-analytical stages, and streamlining the interpretation of intricate laboratory data. Convolutional neural networks (CNNs), one of the deep learning techniques, have greatly enhanced image-based diagnostics, allowing for the quick and precise diagnosis of malaria parasites, urine sediment, and peripheral blood cells. ML-powered clinical decision support systems provide physicians with evidence-based suggestions and real-time insights, improving patient care and clinical outcomes. Despite these developments, issues like data privacy worries and legal barriers still exist, making it necessary to give serious thought to the widespread use of ML in clinical laboratories.

Key words: machine learning, clinical laboratory medicine, predictive modeling, error detection, automated interpretation, clinical decision support systems, convolutional neural networks, image-based diagnostics, data privacy, regulatory challenges