AI, Machine Learning and Deep Learning as Tool for Laboratory Demand Management—The Future of Laboratory Medicine?

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (31 May 2021) | Viewed by 19280

Special Issue Editors

University Hospital Salzburg, Paracelsus Medical University, Department of Laboratory Medicine, Salzburger Landeskliniken, Müllner Hauptstr. 48, 5020 Salzburg, Austria
Institut für Klinische Chemie und Pathobiochemie, Klinikum rechts der Isar der Technischen Universität München, 81675 München, Germany
Munich Municipal Hospital Group, Department for Laboratory Medicine and Microbiology, Klinikum Schwabing, Medizet, Koelner Platz 1, 80804 München, Germany

Special Issue Information

Dear Colleagues,

Inappropriate use of laboratory tests has become a major issue in most healthcare environments. There are several possibilities to counteract this trend, subsumed under the term “laboratory demand management”, most of which focus on reducing the overuse of tests. However, one concept, laboratory diagnostic algorithms, additionally aims to reduce the underuse of tests, making laboratory diagnostics more individualized and patient-centered. This concept encompasses current evidence on the diagnostic workup of the respective disease or symptom as well as algorithms ordering and interpreting test results step by step. As this procedure is unsustainable and prone to error if carried out by laboratory specialists only, IT systems aiding and continuously re-evaluating this process need to be developed. Deep learning algorithms seem to be the most promising solution for this task, as they have been proven to be most effective in other medical diagnostic specialties.

The aim of this Special Issue is therefore to provide an overview of laboratory demand management and laboratory diagnostic algorithms in particular and to depict the current status, requirements, and possibilities of AI, machine learning, and deep learning strategies in laboratory medicine. Finally, we want to paint a predictive picture of our profession in collaboration with other diagnostic disciplines.

Dr. Janne Cadamuro
Dr. Alexander von Meyer
Dr. Andreas Bietenbeck
Guest Editor

Manuscript Submission Information

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Keywords

  • Artificial Intelligence
  • Machine learning
  • Deep learning
  • Laboratory demand management
  • Inappropriate laboratory use
  • Diagnostic error
  • Laboratory professionals
  • Laboratory specialists

Published Papers (5 papers)

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Research

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13 pages, 3621 KiB  
Article
Deep into Laboratory: An Artificial Intelligence Approach to Recommend Laboratory Tests
by Md. Mohaimenul Islam, Tahmina Nasrin Poly, Hsuan-Chia Yang and Yu-Chuan (Jack) Li
Diagnostics 2021, 11(6), 990; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics11060990 - 29 May 2021
Cited by 11 | Viewed by 4171
Abstract
Laboratory tests are performed to make effective clinical decisions. However, inappropriate laboratory test ordering hampers patient care and increases financial burden for healthcare. An automated laboratory test recommendation system can provide rapid and appropriate test selection, potentially improving the workflow to help physicians [...] Read more.
Laboratory tests are performed to make effective clinical decisions. However, inappropriate laboratory test ordering hampers patient care and increases financial burden for healthcare. An automated laboratory test recommendation system can provide rapid and appropriate test selection, potentially improving the workflow to help physicians spend more time treating patients. The main objective of this study was to develop a deep learning-based automated system to recommend appropriate laboratory tests. A retrospective data collection was performed at the National Health Insurance database between 1 January 2013, and 31 December 2013. We included all prescriptions that had at least one laboratory test. A total of 1,463,837 prescriptions from 530,050 unique patients was included in our study. Of these patients, 296,541 were women (55.95%), the range of age was between 1 and 107 years. The deep learning (DL) model achieved a higher area under the receiver operating characteristics curve (AUROC micro = 0.98, and AUROC macro = 0.94). The findings of this study show that the DL model can accurately and efficiently identify laboratory tests. This model can be integrated into existing workflows to reduce under- and over-utilization problems. Full article
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Review

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13 pages, 464 KiB  
Review
Big Data in Laboratory Medicine—FAIR Quality for AI?
by Tobias Ueli Blatter, Harald Witte, Christos Theodoros Nakas and Alexander Benedikt Leichtle
Diagnostics 2022, 12(8), 1923; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics12081923 - 09 Aug 2022
Cited by 8 | Viewed by 4165
Abstract
Laboratory medicine is a digital science. Every large hospital produces a wealth of data each day—from simple numerical results from, e.g., sodium measurements to highly complex output of “-omics” analyses, as well as quality control results and metadata. Processing, connecting, storing, and ordering [...] Read more.
Laboratory medicine is a digital science. Every large hospital produces a wealth of data each day—from simple numerical results from, e.g., sodium measurements to highly complex output of “-omics” analyses, as well as quality control results and metadata. Processing, connecting, storing, and ordering extensive parts of these individual data requires Big Data techniques. Whereas novel technologies such as artificial intelligence and machine learning have exciting application for the augmentation of laboratory medicine, the Big Data concept remains fundamental for any sophisticated data analysis in large databases. To make laboratory medicine data optimally usable for clinical and research purposes, they need to be FAIR: findable, accessible, interoperable, and reusable. This can be achieved, for example, by automated recording, connection of devices, efficient ETL (Extract, Transform, Load) processes, careful data governance, and modern data security solutions. Enriched with clinical data, laboratory medicine data allow a gain in pathophysiological insights, can improve patient care, or can be used to develop reference intervals for diagnostic purposes. Nevertheless, Big Data in laboratory medicine do not come without challenges: the growing number of analyses and data derived from them is a demanding task to be taken care of. Laboratory medicine experts are and will be needed to drive this development, take an active role in the ongoing digitalization, and provide guidance for their clinical colleagues engaging with the laboratory data in research. Full article
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17 pages, 1968 KiB  
Review
Rise of the Machines: The Inevitable Evolution of Medicine and Medical Laboratories Intertwining with Artificial Intelligence—A Narrative Review
by Janne Cadamuro
Diagnostics 2021, 11(8), 1399; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics11081399 - 02 Aug 2021
Cited by 15 | Viewed by 3852
Abstract
Laboratory medicine has evolved from a mainly manual profession, providing few selected test results to a highly automated and standardized medical discipline, generating millions of test results per year. As the next inevitable evolutional step, artificial intelligence (AI) algorithms will need to assist [...] Read more.
Laboratory medicine has evolved from a mainly manual profession, providing few selected test results to a highly automated and standardized medical discipline, generating millions of test results per year. As the next inevitable evolutional step, artificial intelligence (AI) algorithms will need to assist us in structuring and making sense of the masses of diagnostic data collected today. Such systems will be able to connect clinical and diagnostic data and to provide valuable suggestions in diagnosis, prognosis or therapeutic options. They will merge the often so separated worlds of the laboratory and the clinics. When used correctly, it will be a tool, capable of freeing the physicians time so that he/she can refocus on the patient. In this narrative review I therefore aim to provide an overview of what AI is, what applications currently are available in healthcare and in laboratory medicine in particular. I will discuss the challenges and pitfalls of applying AI algorithms and I will elaborate on the question if healthcare workers will be replaced by such systems in the near future. Full article
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13 pages, 422 KiB  
Review
Laboratory Demand Management Strategies—An Overview
by Cornelia Mrazek, Elisabeth Haschke-Becher, Thomas K. Felder, Martin H. Keppel, Hannes Oberkofler and Janne Cadamuro
Diagnostics 2021, 11(7), 1141; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics11071141 - 23 Jun 2021
Cited by 9 | Viewed by 2280
Abstract
Inappropriate laboratory test selection in the form of overutilization as well as underutilization frequently occurs despite available guidelines. There is broad approval among laboratory specialists as well as clinicians that demand management strategies are useful tools to avoid this issue. Most of these [...] Read more.
Inappropriate laboratory test selection in the form of overutilization as well as underutilization frequently occurs despite available guidelines. There is broad approval among laboratory specialists as well as clinicians that demand management strategies are useful tools to avoid this issue. Most of these tools are based on automated algorithms or other types of machine learning. This review summarizes the available demand management strategies that may be adopted to local settings. We believe that artificial intelligence may help to further improve these available tools. Full article
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38 pages, 1505 KiB  
Review
Has the Flood Entered the Basement? A Systematic Literature Review about Machine Learning in Laboratory Medicine
by Luca Ronzio, Federico Cabitza, Alessandro Barbaro and Giuseppe Banfi
Diagnostics 2021, 11(2), 372; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics11020372 - 22 Feb 2021
Cited by 19 | Viewed by 3118
Abstract
This article presents a systematic literature review that expands and updates a previous review on the application of machine learning to laboratory medicine. We used Scopus and PubMed to collect, select and analyse the papers published from 2017 to the present in order [...] Read more.
This article presents a systematic literature review that expands and updates a previous review on the application of machine learning to laboratory medicine. We used Scopus and PubMed to collect, select and analyse the papers published from 2017 to the present in order to highlight the main studies that have applied machine learning techniques to haematochemical parameters and to review their diagnostic and prognostic performance. In doing so, we aim to address the question we asked three years ago about the potential of these techniques in laboratory medicine and the need to leverage a tool that was still under-utilised at that time. Full article
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