Clinical application of intelligent technologies and integration in medical laboratories

Abstract

With the development of scientific technology, the transition to the intelligent era of digitalization and automation is an irresistible trend for medical laboratories. Medical diagnosis systems have undergone significant changes as a result of intelligent technologies, such as machine learning, artificial intelligence, and the Internet of Things, from the collection, transmission, and detection of test samples to the review of reports and the provision of clinical feedback. In addition to significantly enhancing the efficiency, consistency, and accuracy of medical laboratory testing, these technologies also assist the improvement of individualized healthcare and medical expert systems, as well as the early detection and treatment of diseases. The future development of medical laboratories will focus on integrating big data and diverse intelligent resources, cooperating more closely with clinical departments, and realizing the effective pathway of patient‐centered care. The purpose of this review is to illustrate the current state of intelligent technology integration in medical laboratories and provide a preliminary discussion about the potential future influences of intelligent technology development on the evolution of medical laboratories.

From bench to bedside: Opportunities and challenges for iLABMED

“…They believed that AI-based novel technologies greatly enhance the efficiency, consistency, and accuracy of laboratory examinations. Conventional laboratory medicine will ultimately enter a new digital and AI-based era [21].…”

Candidate reference measurement procedure for serum 17‐hydroxyprogesterone quantification via isotope dilution liquid chromatography–tandem mass spectrometry

“…With the advancement of automation technologies, the integration of automated processing equipment into the pre-treatment steps may potentially address the drawbacks of this cRMP. Additionally, considering the absence of recognized RMPs for the determination of serum tumor markers, our next endeavor involves the application of LC-MS/MS to develop intelligent, integrated solutions for quantifying serum tumor markers [29,30].…”