Journal of Ai ML DL
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Journal of Ai ML DL

Volume 1 Number 1 2025

Article Contents

REVIEWS   (Open Access)
Contents Vol 1 (1)

Artificial Intelligence and Applied Machine Learning to Improve Pre-Analytical and Post-Analytical Processes in Laboratory Medicine

Md. Moyen Uddin PK1*, Tufael2

+ Author Affiliations

Journal of Ai ML DL 1 (1) 1-10 https://doi.org/10.25163/ai.1110407

Submitted: 27 July 2025 Revised: 29 September 2025  Published: 05 October 2025 

Abstract

Artificial intelligence (AI) and machine learning (ML) are transforming laboratory medicine by enhancing accuracy, efficiency, and reliability across pre-analytical and post-analytical phases. The pre-analytical stage, which involves patient preparation, sample collection, labeling, transport, and processing, is particularly vulnerable to human error. Studies indicate that nearly 30% of laboratory errors occur at this stage, often due to mislabeling, mishandling, or improper preparation. AI-driven systems, including robotic phlebotomy devices, smart barcoding, and predictive analytics, significantly reduce these risks by automating repetitive tasks, monitoring sample integrity in real time, and anticipating workflow bottlenecks. Similarly, ML algorithms integrated into Laboratory Information Systems (LIS) enhance interoperability with Electronic Health Records (EHR), enabling seamless data tracking and reducing delays. In the post-analytical phase, AI and ML play a crucial role in validating results, detecting anomalies, and supporting clinical decision-making. Deep learning models applied to pathology images improve diagnostic accuracy, while natural language processing (NLP) tools standardize laboratory reports, making them clearer and more consistent. Predictive models further allow prioritization of urgent results and facilitate personalized treatment planning by integrating laboratory data with imaging and clinical histories. These innovations not only reduce turnaround time but also improve patient outcomes by enabling earlier diagnosis and tailored therapeutic interventions. Despite these advancements, challenges such as algorithmic bias, data privacy, system interoperability, and the interpretability of AI models remain.

Keywords: Artificial Intelligence, Machine Learning, Laboratory Medicine, Diagnostics, Precision Healthcare

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