Diagnostic labs have traditionally been designed by iterating upon manual processes and then inserting robotic technology “where it makes sense” to automate a particular task.
However, creating the lab of the future requires a different approach. It means flipping the script to be fully automated at its core and then identifying “where to add unique value” with human intervention.
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In lab operations, there are certain places where human judgement is critical. For example, when creativity or innovation is required to solve a problem, or we need to make judgements of ethics with empathy. Humans are better at evaluating the validity or data anomalies (think “garbage in/garbage out” mode where humans determine legitimacy). When quality and accuracy are important, human + robot = optimum result.
In practice, this approach requires investment, commitment, and process maturity. Maintaining quality control presents challenges that require constant monitoring, calibration, and investigation. While automation may reduce human error, it can introduce new errors, such as those caused by system malfunctions or incorrect/inconsistent programming or versioning.
Accordingly, a human needs to review the solution to determine if those processes are working correctly, problems are solved effectively and engineer the next generation of models. The first step, though, is creating the next-generation lab.
The lab of the future
In building the lab of tomorrow, we push innovation into all components: The wet lab assays are developed and refined for sensitivity, specificity, robustness, and cost effectiveness. The hardware uses automated instruments (e.g., liquid handlers, PCR, data acquisition) and the robotic backbone connects all these instruments with six-axis arms, slides, tube sorters, etc. Finally, operating procedures are established for how to service the instruments, resolve failures, monitor for data quality, run validation, and conduct ongoing reagent/instrument/competency quality control experiments.
The final element in the lab of the future is the software infrastructure. There are five central components to ensure that your lab is capable of operating hundreds of instruments to process thousands of clinical samples per day with unparalleled speed and accuracy:
1. Flexible graph data architecture – Your database must be adaptable and scalable to ensure that all unique workflows in the lab can change as quickly as you evolve. The flexible graph representation can capture all elements (nodes) and steps (edges) in a process by storing information in graph format, which enables complex relationships and ontological queries to be conducted dynamically.
2. Batch queuing systems and dynamic load balancing – A lab only runs efficiently if all resources are used to maximum productivity. Dynamic load balancing adjusts resource allocation in real time, while batch queuing provides a way to manage the flow of tasks programmatically and recover from failures without human intervention.
3. Real-time scheduling – All the laboratory equipment must interact in a coordinated, synchronized way to execute multiple processes in parallel. Precise scheduling directs robotic arms, thermocyclers, liquid handlers, and many other co-located pieces of equipment when to start specific processing steps to accomplish the overall engineered workflow—all working in concert to prevent deadlocks.
4. Standardized, networked and data-interfaced components – Another important part of the infrastructure is having components that enable data transfer to report back to the graph database and receive communication from the scheduling system. Building adapters to ensure commonality among the many systems, vendors, and devices.
5. Data science tools – Data is only valuable if it can be interpreted and used both for real-time monitoring and retrospective analytics. Accordingly, data science tools can help laboratory workers analyze, visualize, and share data effectively, especially when that data is high in volume and derived from complex workflows.
Data-driven insights
Once the lab of the future is built, the next consideration is lab operations—and/or how to ensure data is working effectively for you.
Data is the lifeblood of any lab, but the biggest obstacle is organizing and using that data. By its nature, data coming in and out of the lab is in a myriad of different formats, being interpreted and used in an infinite number of ways—creating issues of consistency and interoperability. The software and architecture discussed above plays a huge role in solving future data harmonization problems.
Once the data inputs and output are consistent and speaking the “same language,” then we can effectively deploy and link to the massive data generators like next-generation sequencing (NGS), which has revolutionized genomic research by enabling rapid and relatively affordable acquisition of multi-omics data of germline, somatic, RNA, spatial, etc. This allows for comprehensive analysis of genetic variations and mutations that provide insight into the roots of disease. This data is instrumental in identifying new therapeutic targets, which helps the development of personalized treatment plans based on an individual’s unique genetic profile.
The role of AI
A rule of thumb for what technology can and can’t do to replace human interaction is summed up in a recent Harvard Business Review article by Andrew Ng: “If a typical person can do a mental task with less than one second of thought, we can probably automate it using AI either now or in the near future.”
AI-driven automation can streamline repetitive workflows, cutting manual interventions, which minimizes the risk of human error. Machine-learning models can be trained to solve data-intensive problems in classification, clustering, and prediction—compromising the bulk of primary data analysis. Hardware automation is deployed pervasively, handling the bulk of sample processing, liquid transfers, and material movement, delivering optimal monitoring and control capabilities. Importantly, AI can automatically detect anomalies and flag patterns and insights in a fraction of the time that even the fastest human can do.
This isn’t a “set it and forget it” approach though: When using AI in production, there is a human supervisory role both in the assay creation, training, and validation to ensure accuracy on both real and simulated cases. Scientists will need to continuously monitor and review data—including inspecting quality control plots and trending performance metrics—to ensure all clinically impactful results are meaningful and valid.
Yet, the application of AI in genomic labs extends beyond data analysis. Predictive models are capable of forecasting disease progression and treatment responses on an individual level, allowing for more personalized and effective treatment plans while generative AI can both summarize and help translate the vast knowledge base from literature to scientists and practitioners.
AI can be tasked with identifying anomalies that correlate with specific mechanisms of disease. If a target is identified, simulations can model the protein within its active site, allowing for perturbations to uncover potential druggable regions. Once a viable target is found, the focus shifts to creating candidate designs, optimizing molecular design, and assessing different configurations. Using this information, scientists can evaluate their metabolic signatures, toxicity, and likelihood of success in clinical trials. These techniques accelerate the overall speed and accuracy of data analysis, which is resulting in the discovery of new biomarkers and therapeutic targets, ultimately facilitating earlier diagnoses and prognoses of diseases.
Machine learning and AI-enabled diagnostics are transforming the way clinicians approach cancer patient care. For example, urologists and radiation oncologists could use both molecular and AI-powered testing solutions to inform decisions both before treatment at the time of biopsy for active surveillance and following surgery or radiation treatment. Having both genetic and morphologic insights at the time of biopsy combined with the enhanced ability to predict disease recurrence after initial therapy can lead to more informed treatment decisions and enhance the potential for better patient outcomes.
Benefits of automated diagnostic labs
The lab of the future runs 24 hours a day/seven days a week. As a result, they’re designed to run economically, sustain growth, and have increased utilization.
Most importantly, improving the speed at which these labs run means that reports can deliver vital information to patients and healthcare providers when they are making critical medical decisions.
As precision medicine advances, the adoption of NGS, gene editing, and AI is transforming genomic research and clinical practice. These advancements are enabling more personalized, predictive, and preventive healthcare solutions, ultimately working to improve patient outcomes.
