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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 EMAIL logo 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;57:802–11.10.1515/cclm-2018-1323Search in Google Scholar

2. Hawker CD. Nonanalytic laboratory automation: a quarter century of progress. Clin Chem 2017;63:1074–82.10.1373/clinchem.2017.272047Search in Google Scholar

3. Hoffmann GE. Concepts for the third generation of laboratory systems. Clin Chim Acta 1998;278:203–16.10.1016/S0009-8981(98)00147-8Search in Google Scholar

4. MarketsandMarkets. Artificial Intelligence in healthcare market worth $36.1 billion by 2025. 2018 [February 2019]. Available from: https://www.marketsandmarkets.com/PressReleases/artificial-intelligence-healthcare.asp.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: https://www.marketsandmarkets.com/Market-Reports/artificial-intelligence-market-74851580.html.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;101:158–68.10.1016/j.comnet.2015.12.017Search 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;3:18–23.10.1016/j.mfglet.2014.12.001Search 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;17:1–15.10.1155/S1463924695000010Search in Google Scholar PubMed PubMed Central

9. Ialongo C, Pieri M, Bernardini S. Artificial Neural Network for Total Laboratory Automation to improve the management of sample dilution. SLAS Technol 2017;22:44–9.10.1177/2211068216636635Search in Google Scholar PubMed

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;55:231–6.10.1515/cclm-2016-0263Search in Google Scholar PubMed

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

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