Your AI Is Only as Good as Your Inputs

Augustė Užuotaitė, an R&D Supervisor at Thermo Fisher Scientific's Genetic Sciences lab, highlights the importance of AI and automation in reducing manual variability and streamlining workflows. However, the foundation of these systems relies on the quality of consumables such as reagents, master mixes, primers, and probes. If these components are inconsistent, AI systems can amplify even minor issues, leading to batch-level failures.

The credibility of genetic analysis, built on reproducibility, depends heavily on the stability of these consumables. Automation can introduce errors if the underlying consumables do not perform reliably, as subtle changes like evaporation or incomplete mixing can go unnoticed and affect the results. The quality of consumables is particularly crucial in multiplex qPCR assays, where noise sources can compete with the real signal, resulting in curves that are difficult to interpret and translate into action.

To mitigate these risks, labs should thoroughly evaluate the performance of consumables under real automated conditions before scaling up. This includes assessing factors like hold times, ambient exposure, and mixing steps. Additionally, labs must consider the potential pitfalls of assuming sterility or general compatibility alone, as these certifications do not guarantee the stability of critical components.

Genetic analysis labs can take practical steps to ensure consumable reliability, such as testing new lots under worst-case conditions, including low input, edge wells, and extended hold times. AI systems can aid in identifying issues early, but their effectiveness hinges on the trustworthiness of the physical inputs and comprehensive metadata.

AI-driven quality monitoring can flag subtle trends, but without robust traceability, it may fail to identify root causes or normalize unstable baselines.