Accessible Unlicensed Requires Authentication Published by De Gruyter April 8, 2019

Total laboratory automation has the potential to be the field of application of artificial intelligence: the cyber-physical system and “Automation 4.0”

Cristiano Ialongo and Sergio Bernardini

Corresponding author: Dr. Med. Cristiano Ialongo, Department of Physiology and Pharmacology, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome (RM), Italy, Phone: +3906-4991-2987

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: None declared.

  3. Employment or leadership: None declared.

  4. Honorarium: None declared.

  5. Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.

References

1. Lippi G, Da Rin G. Advantages and limitations of total laboratory automation: a personal overview. Clin Chem Lab Med 2019;57:802–11.Search in Google Scholar

2. Hawker CD. Nonanalytic laboratory automation: a quarter century of progress. Clin Chem 2017;63:1074–82.Search in Google Scholar

3. Hoffmann GE. Concepts for the third generation of laboratory systems. Clin Chim Acta 1998;278:203–16.Search in Google Scholar

4. MarketsandMarkets. Artificial Intelligence in healthcare market worth $36.1 billion by 2025. 2018 [February 2019]. Available from: .Search in Google Scholar

5. MarketsandMarkets. Artificial Intelligence market by offering (hardware, software, services), technology (machine learning, natural language processing, context-aware computing, computer vision), end-user industry, and geography – global forecast to 2025 2018. [February 2019]. Available from: .Search in Google Scholar

6. Wang S, Wan J, Zhang D, Li D, Zhang C. Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination. Comput Networks 2016;101:158–68.Search in Google Scholar

7. Lee J, Bagheri B, Kao H-A. A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems. Manuf Lett 2015;3:18–23.Search in Google Scholar

8. Place JF, Truchaud A, Ozawa K, Pardue H, Schnipelsky P. Use of artificial intelligence in analytical systems for the clinical laboratory. J Automat Chem 1995;17:1–15.Search in Google Scholar

9. Ialongo C, Pieri M, Bernardini S. Artificial Neural Network for Total Laboratory Automation to improve the management of sample dilution. SLAS Technol 2017;22:44–9.Search in Google Scholar

10. Ialongo C, Pieri M, Bernardini S. Smart management of sample dilution using an artificial neural network to achieve streamlined processes and saving resources: the automated nephelometric testing of serum free light chain as case study. Clin Chem Lab Med 2017;55:231–6.Search in Google Scholar

Received: 2019-02-27
Accepted: 2019-03-15
Published Online: 2019-04-08
Published in Print: 2019-10-25

©2019 Walter de Gruyter GmbH, Berlin/Boston