Artificial intelligence (AI) is gaining a stronger foothold in biomedical and research applications. Hospitals use AI to predict patient readmission and assist in diagnostic procedures. Research labs use it in analyzing image data and finding connections between published papers. The underlying mechanism that makes modern AI so useful in these contexts is its ability to learn patterns from data. Because of its ability to pick up on patterns faster than humans—and even detect patterns imperceptible to humans—AI is ideal for synthesizing, calculating, and extrapolating data, making it the optimal tool for predicting future events. AI can accurately determine the probability of certain events occurring given specific patterns in a dataset.
Labs can reap huge benefits from this capability, especially in the realm of asset management, with intelligent equipment monitoring and usage optimization. Machines in the lab inevitably break down, either due to wear or misuse. In most cases, telltale signs can appear before a major malfunction occurs. But some of these signs are not so obvious, and this is where AI can help out.
Monitoring equipment performance and health
Take an ultra-low temperature (ULT) freezer as an example. ULT freezers must provide consistent temperatures to preserve biological samples for extended periods. Usually, they operate at -80ºC, but they might not always be at -80ºC flat. There may be slight variations of a few tenths or hundredths of a degree. Normally such small variations are acceptable and won’t compromise the samples. But larger variations can deteriorate samples. These significant temperature fluctuations may be a sign of a malfunction or an impending failure. To a human being, these fluctuations may not be apparent, but to an AI they are. Besides temperature trends, AI can also monitor other data points like compressor uptime, ambient temperature, and electricity usage.
The pattern recognition capabilities of AI expand beyond passive monitoring.
If the AI detects trends in the data correlated with freezer failure, it can automatically send an alert to lab managers or staff.
Optimizing equipment use
The pattern recognition capabilities of AI expand beyond passive monitoring. AI can also actively recommend changes to a lab’s workflow or the way equipment is used, boosting efficiency and cutting costs. For example, an AI can determine the relationship between sample placement and interior temperature in a ULT freezer. The AI can then recommend how best to arrange samples in the freezer to ensure consistency of temperature throughout the whole interior.
Looking ahead, AI and other emerging technologies like the metaverse may provide enhanced communication and troubleshooting abilities, allowing researchers to resolve issues easier than ever before.