Lab automation or Laboratory automation is a multi-disciplinary strategy to develop, research and optimize technologies in the clinical laboratory. The application of this technology in laboratories helps to achieve higher levels of performance in less time. Laboratory automation aids increasing productivity, reducing lab process cycle times, elevating experimental data quality and enabling easy experimentation. Moreover, the system includes development of the laboratory information (management) systems (LIS/LIMS) and improvement of pre- and post-analytic automation. The application of this technology in laboratories is to achieve higher levels of performance and eliminates human errors.

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Lab automation is expected to continue to infiltrate labs over the coming years, with new technologies being developed and adopted. However, several challenges need to be addressed before lab automation can reach its full potential.

LIMS enables researchers to automate their workflows, integrate their instruments, and generate accurate and reliable results with speed and precision. LIMS can also help in tracking data from sequencing that occurs over time and between multiple experiments.

These systems are expected to be commonplace in automated labs of the future. It is also expected that the technology offered by these systems will continue to grow and develop, adding further benefits to introducing lab automation systems.

One particular area of science that is expected to rapidly adopt automation processes over the coming years is the field of molecular biology. Recent research has demonstrated that automated systems are capable of handling delicate biological samples in a way that does not impact the results.

Challenges in lab automation

Several challenges need to be overcome before lab automation can reach its full potential. Firstly, the conversion from manual to automated systems requires re-training staff, who are used to working with the samples with their hands, to be able to operate the technological systems instead.

Staff may not always have the same level of expertise when it comes to computer systems, which can present an extra cost and time expense in needing to provide training.

Because of this need to retrain, there is often a learning curve involved when new automated systems are introduced. This means that there is a period while staff are still acquiring skills and knowledge before the system can operate at its full efficiency.

The key players profiled in this report include Agilent Technologies, Inc., BioMerieux SA, Danaher Corporation, F. Hoffmann-La Roche AG, Hamilton Robotics, PerkinElmer, Inc., Qiagen N.V., Siemens AG, Tecan Group Ltd., and Thermo Fisher Scientific Inc.

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