AIMC Topic: Laboratories, Clinical
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Artificial Intelligence in the Clinical Laboratory: An Overview with Frequently Asked Questions.
Clinics in laboratory medicine
Dec 13, 2022
This article provides an overview of machine learning fundamentals and some applications of machine learning to clinical laboratory diagnostics and patient management. A key goal of this article is to provide a basic foundation in clinical machine le...
Artificial intelligence: is it the right time for clinical laboratories?
Clinical chemistry and laboratory medicine
Oct 24, 2022
Clinlabomics: leveraging clinical laboratory data by data mining strategies.
BMC bioinformatics
Sep 24, 2022
The recent global focus on big data in medicine has been associated with the rise of artificial intelligence (AI) in diagnosis and decision-making following recent advances in computer technology. Up to now, AI has been applied to various aspects of ...
Energy Efficiency of Inference Algorithms for Clinical Laboratory Data Sets: Green Artificial Intelligence Study.
Journal of medical Internet research
Jan 25, 2022
BACKGROUND: The use of artificial intelligence (AI) in the medical domain has attracted considerable research interest. Inference applications in the medical domain require energy-efficient AI models. In contrast to other types of data in visual AI, ...
A highly accurate delta check method using deep learning for detection of sample mix-up in the clinical laboratory.
Clinical chemistry and laboratory medicine
Dec 29, 2021
OBJECTIVES: Delta check (DC) is widely used for detecting sample mix-up. Owing to the inadequate error detection and high false-positive rate, the implementation of DC in real-world settings is labor-intensive and rarely capable of absolute detection...
Disruptive innovations in the clinical laboratory: catching the wave of precision diagnostics.
Critical reviews in clinical laboratory sciences
Jul 23, 2021
Disruptive innovation is an invention that disrupts an existing market and creates a new one by providing a different set of values, which ultimately overtakes the existing market. Typically, when disruptive innovations are introduced, their performa...
Identifying mislabelled samples: Machine learning models exceed human performance.
Annals of clinical biochemistry
Jul 16, 2021
BACKGROUND: It is difficult for clinical laboratories to identify samples that are labelled with the details of an incorrect patient. Many laboratories screen for these errors with delta checks, with final decision-making based on manual review of re...
General Applicability of Existing College of American Pathologists Accreditation Requirements to Clinical Implementation of Machine Learning-Based Methods in Molecular Oncology Testing.
Archives of pathology & laboratory medicine
Apr 1, 2025
CONTEXT.—: The College of American Pathologists (CAP) accreditation requirements for clinical laboratory testing help ensure laboratories implement and maintain systems and processes that are associated with quality. Machine learning (ML)-based model...
Regulation of artificial intelligence in healthcare: Clinical Laboratory Improvement Amendments (CLIA) as a model.
Journal of the American Medical Informatics Association : JAMIA
Feb 1, 2025
OBJECTIVES: To assess the potential to adapt an existing technology regulatory model, namely the Clinical Laboratory Improvement Amendments (CLIA), for clinical artificial intelligence (AI).
Validating, Implementing, and Monitoring Machine Learning Solutions in the Clinical Laboratory Safely and Effectively.
Clinical chemistry
Nov 4, 2024
BACKGROUND: Machine learning solutions offer tremendous promise for improving clinical and laboratory operations in pathology. Proof-of-concept descriptions of these approaches have become commonplace in laboratory medicine literature, but only a sca...
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