Recent advancements in laboratory automation technology and their impact on scientific research and laboratory procedures

https://doi.org/10.53730/ijhs.v7nS1.14680

Authors

  • Abdullah Omar Mohammed Omair Lab specialist, Regional مab in Madinah, 0504564755
  • Abeer Mohammed Abdul Jabbar Lab Technician, King Fahd Hospital in Medina, 0538058990
  • Mustafa Othman Albulushi Lab Specialist, Nujood Medical Center, 0560685961

Keywords:

laboratory automation, technology, scientific research, laboratory procedures, advancemen

Abstract

This article examines the latest developments in laboratory automation technologies and their influence on scientific research and laboratory protocols. The research examines the incorporation of robotic sample handling systems, artificial intelligence and machine learning algorithms, sophisticated software and hardware, and safety improvements in laboratory automation systems. The research emphasizes the advantages of laboratory automation technologies, such as improved efficiency, repeatability, and safety in the laboratory setting. The study also examines the ramifications of automation technology on scientific research, including the hastening of scientific advancements and the creation of innovative remedies and cures.  Moreover, the study highlights the obstacles linked to the adoption of sophisticated automation systems, such as the financial and intricate nature of these systems, and the need for specialized education and proficiency. The review further outlines potential future developments in laboratory automation technology, including continued progress in robotics, artificial intelligence, and microfluidics. It also highlights the potential integration of automation technology with new disciplines like synthetic biology and precision medicine. In summary, the research emphasizes the significant impact of laboratory automation technologies in pushing the boundaries of scientific knowledge and innovation.

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References

Abramo, G., & D’Angelo, C. A. (2014). How do you define and measure research productivity?. Scientometrics, 101, 1129-1144.

Archetti, C., Montanelli, A., Finazzi, D., Caimi, L., & Garrafa, E. (2017). Clinical laboratory automation: a case study. Journal of public health research, 6(1), jphr-2017.

Benchoufi, M., & Ravaud, P. (2017). Blockchain technology for improving clinical research quality. Trials, 18(1), 1-5.

Caragher, T. E., Lifshitz, M. S., & DeCresce, R. (2017). Analysis: clinical laboratory automation. Henry’s Clinical Diagnosis and Management by Laboratory Methods, 60-65.

Ceroni, J. A. (2023). Economic Rationalization of Automation Projects and Quality of Service. In Springer Handbook of Automation (pp. 683-698). Cham: Springer International Publishing.

Daniszewski, M., Crombie, D. E., Henderson, R., Liang, H. H., Wong, R. C., Hewitt, A. W., & Pébay, A. (2018). Automated cell culture systems and their applications to human pluripotent stem cell studies. SLAS TECHNOLOGY: Translating Life Sciences Innovation, 23(4), 315-325.

Dara, S., Dhamercherla, S., Jadav, S. S., Babu, C. M., & Ahsan, M. J. (2022). Machine learning in drug discovery: a review. Artificial Intelligence Review, 55(3), 1947-1999.

De Bruyne, S., Speeckaert, M. M., Van Biesen, W., & Delanghe, J. R. (2021). Recent evolutions of machine learning applications in clinical laboratory medicine. Critical reviews in clinical laboratory sciences, 58(2), 131-152.

Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... & Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994.

Florian, D. C., Odziomek, M., Ock, C. L., Chen, H., & Guelcher, S. A. (2020). Principles of computer-controlled linear motion applied to an open-source affordable liquid handler for automated micropipetting. Scientific Reports, 10(1), 13663.

Goodman, S. N., Fanelli, D., & Ioannidis, J. P. (2016). What does research reproducibility mean?. Science translational medicine, 8(341), 341ps12-341ps12.

Hammer, A. J., Leonov, A. I., Bell, N. L., & Cronin, L. (2021). Chemputation and the standardization of chemical informatics. JACS Au, 1(10), 1572-1587.

Hawker, C. D., Genzen, J. R., & Wittwer, C. T. (2017). Automation in the clinical laboratory. Tietz Textbook of Clinical Chemistr y and Molecular Diagnostics, Elsevier, Eds, 370, e1-e24.

Holland, I., & Davies, J. A. (2020). Automation in the life science research laboratory. Frontiers in Bioengineering and Biotechnology, 8, 571777.

Hua, S., De Matos, M. B., Metselaar, J. M., & Storm, G. (2018). Current trends and challenges in the clinical translation of nanoparticulate nanomedicines: pathways for translational development and commercialization. Frontiers in pharmacology, 9, 790.

Jessop-Fabre, M. M., & Sonnenschein, N. (2019). Improving reproducibility in synthetic biology. Frontiers in bioengineering and biotechnology, 7, 18.

Kitney, R., Adeogun, M., Fujishima, Y., Goñi-Moreno, Á., Johnson, R., Maxon, M., ... & Philp, J. (2019). Enabling the advanced bioeconomy through public policy supporting biofoundries and engineering biology. Trends in biotechnology, 37(9), 917-920.

Klaus, B., & del Alamo, D. (2018). Talent Identification at the limits of Peer Review: an analysis of the EMBO Postdoctoral Fellowships Selection Process. bioRxiv, 481655.

Koelmel, J. P., Kroeger, N. M., Ulmer, C. Z., Bowden, J. A., Patterson, R. E., Cochran, J. A., ... & Yost, R. A. (2017). LipidMatch: an automated workflow for rule-based lipid identification using untargeted high-resolution tandem mass spectrometry data. BMC bioinformatics, 18, 1-11.

Lou, A. H., Elnenaei, M. O., Sadek, I., Thompson, S., Crocker, B. D., & Nassar, B. (2016). Evaluation of the impact of a total automation system in a large core laboratory on turnaround time. Clinical Biochemistry, 49(16-17), 1254-1258.

Mellingwood, C. R. (2019). Amphibious researchers: working with laboratory automation in synthetic biology.

Miles, B., & Lee, P. L. (2018). Achieving reproducibility and closed-loop automation in biological experimentation with an IoT-enabled lab of the future. SLAS TECHNOLOGY: Translating Life Sciences Innovation, 23(5), 432-439.

Naugler, C., & Church, D. L. (2019). Automation and artificial intelligence in the clinical laboratory. Critical reviews in clinical laboratory sciences, 56(2), 98-110.

Rahmanian, F., Flowers, J., Guevarra, D., Richter, M., Fichtner, M., Donnely, P., ... & Stein, H. S. (2022). Enabling Modular Autonomous Feedback‐Loops in Materials Science through Hierarchical Experimental Laboratory Automation and Orchestration. Advanced Materials Interfaces, 9(8), 2101987.

Rashidi, H. H., Tran, N., Albahra, S., & Dang, L. T. (2021). Machine learning in health care and laboratory medicine: General overview of supervised learning and Auto‐ML. International Journal of Laboratory Hematology, 43, 15-22.

Schneider, G. (2018). Automating drug discovery. Nature reviews drug discovery, 17(2), 97-113.

Singh, S., Kumar, R., Payra, S., & Singh, S. K. (2023). Artificial Intelligence and Machine Learning in Pharmacological Research: Bridging the Gap Between Data and Drug Discovery. Cureus, 15(8).

Song, Y. K., Hong, S. H., Eo, S., & Shim, W. J. (2021). A comparison of spectroscopic analysis methods for microplastics: manual, semi-automated, and automated Fourier transform infrared and Raman techniques. Marine pollution bulletin, 173, 113101.

Stein, H. S., & Gregoire, J. M. (2019). Progress and prospects for accelerating materials science with automated and autonomous workflows. Chemical science, 10(42), 9640-9649.

Xie, Y., Sattari, K., Zhang, C., & Lin, J. (2023). Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation. Progress in Materials Science, 132, 101043.

Yu, H. Y. E., Lanzoni, H., Steffen, T., Derr, W., Cannon, K., Contreras, J., & Olson, J. E. (2019). Improving laboratory processes with total laboratory automation. Laboratory Medicine, 50(1), 96-102.

Published

26-12-2023

How to Cite

Omair, A. O. M., Jabbar, A. M. A., & Albulushi, M. O. (2023). Recent advancements in laboratory automation technology and their impact on scientific research and laboratory procedures. International Journal of Health Sciences, 7(S1), 3043–3052. https://doi.org/10.53730/ijhs.v7nS1.14680

Issue

Section

Peer Review Articles