Here, we discuss what lab automation is and how it is being used to improve clinical diagnostics. We also consider the benefits of the use of lab automation in this field and hypothesize how lab automation in diagnostics may evolve in the future.
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Lab automation is making waves in the healthcare sector. Its evolution has been underway since the emergence of early automated systems in the 1950s. Now, lab automation technology has begun to mature and is bringing numerous benefits to the fields of science that leverage it. Diagnostics is one such field that is taking advantage of the recent advances in lab automation. Here, lab automation is being used to enhance and optimize various processes, from sample processing to sample preparation and analysis.
What Is Lab Automation and How Is It Revolutionizing Healthcare?
Lab automation was born out of the boom in industrial growth and the increased desire for automated equipment that happened in the 1920s. In the years that followed, scientists began to develop their first automated lab technologies. The very first true stand-alone automation lab technology emerged in the 1950s. Since then, lab automation equipment has been introduced to conduct a vast array of tasks, such as those involved in sample preparation, analysis, and data handling.
Today, key features of lab automation systems include robots (e.g. robotic arms and automated platforms to carry out sample handling, pipetting, plate manipulation, and more), automated instruments (such as automated liquid handlers, sample analyzers, and high-throughput screening systems), laboratory information management systems (LIMS), data analysis and interpretation software, and quality control mechanisms.
As lab automation systems have become more sophisticated, the technology has become widely adopted and is revolutionizing the healthcare sector in many ways. For example, lab automation is improving the quality and standardization of testing procedures, optimizing resource allocation, streamlining workflows, reducing overheads, and increasing the overall throughput of testing systems. It is also leading to the development of faster and more accurate diagnostic tools.
How Is Lab Automation Being Used in Diagnostics?
In diagnostics, lab automation is being used to improve the accuracy, efficiency, and throughput of diagnostic tests. First, lab automation is being leveraged to produce automated systems that carry out sample handling and processing tasks. This reduces human error, enhances sample tracking, and ensures samples are processed consistently.
Robotic liquid handlers are also commonly used for the precise and accurate dispensing of liquid samples, which helps to improve the accuracy of diagnostic tests.
High-throughput screening is possible thanks to automated systems. Without automation, there would be a limit to how many simultaneous analyses could be run for specific markers.
Flow cytometry is used to analyze cells in a stream of fluid. It is most often used for immunophenotyping in the diagnosis of some types of cancer and blood diseases. Lab automation has enabled the automation of flow cytometers, which has greatly helped in aiding disease identification.
Molecular diagnostics also relies on lab automation, particularly in PCR (polymerase chain reaction) and nucleic acid sequencing.
The COVID-19 pandemic presents a good case example of how lab automation is being used in diagnostics. With a huge uptick in demand for testing, without lab automation, it would not have been possible to process the vast amount of tests that were being carried out. Examples of high throughput automatic testing equipment capable of analyzing vast amounts of samples, as was necessary during the pandemic, include the Thermo Fisher Scientific Amplitude Solution, the ADVIA Centaur XPT Immunoassay System, and the Atellica IM Analyzer System.
The Benefits of Lab Automation for Diagnostics
Utilizing lab automation for diagnostics has a wide range of benefits. Some have already been touched on above. Overall, these benefits serve to improve the accuracy and efficiency of diagnostic tools, and by doing so, it is possible to improve patient outcomes through early disease identification and disease classification.
By streamlining workflow and minimizing manual handling, lab automation increases the efficiency of diagnostics processes. It also allows human workers to be redeployed to more meaningful work rather than manual tasks. Automation also reduces human errors, ensuring the accuracy and precision of these tests.
An obvious benefit of lab automation is that it speeds up processing times, meaning that the results of diagnostic tests can be more quickly obtained. By automizing processes, improving levels of standardization can be achieved. This means that processes can be carried out consistently and reliably. High throughput screening is a key benefit of lab automation in diagnostics. As mentioned above, this allows for the simultaneous screening of high volumes of samples - allowing clinical laboratories to process high volumes of samples at the same time, which improves the turnaround of diagnostic tests.
The Future of Lab Automation in Diagnostics
In the future, advancements in technology will continue to push forward the use of lab automation in diagnostics. In the coming years, we will likely see the increasing integration of artificial intelligence (AI) and machine learning algorithms in diagnostics. For example, such algorithms may be used for data analysis and interpretation to increase the accuracy of diagnostic tools and provide valuable insights for personalized medicine.
In addition, as robotics technology continues to advance, it is likely that we will see the development of more sophisticated robotics with new capabilities emerge for use in automated tasks in clinical diagnostics.
References and Further Reading
Al‐Attar, A. et al. (2023). Automation in flow cytometry: Guidelines and review of systems. Cytometry Part B: Clinical Cytometry [Preprint]. doi.org/10.1002/cyto.b.22125.
Mader, C. & Mader, G. (1953). Automatic Volume Fraction Collector. Analytical Chemistry, 25(9), pp. 1423–1423. doi.org/10.1021/ac60081a042.
Martin, S.M. (1958). Automatic starter for chromatograms. Analytical Chemistry, 30(11), pp. 1890–1890. doi.org/10.1021/ac60143a639.
Olsen, K. (2012). The first 110 years of laboratory automation: Technologies, applications, and the creative scientist. SLAS Technology, 17(6), pp. 469–480. doi.org/10.1177/2211068212455631.
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