If you’ve ever been sent down the rabbit hole of modern diagnostics - one test leading to another, each pricier than the last - you know medicine could learn a thing or two from electronics. In Electronic Design Automation (EDA), engineers have specific tests for specific faults: “stuck-at-1,” “timing violation,” “power leak.” Run the right diagnostics, and the chip tells you exactly where it’s broken.
In medicine, by contrast, we’ve got a galaxy of overlapping tests — blood panels, genomic assays, MRI sequences - and no consensus on which ones actually tell the whole story. It’s a field that still runs partly on intuition, luck, and insurance coverage.
Enter Dynamic Sensor Selection, a term that sounds like something you’d use to debug a Mars rover but is actually from a 2025 paper by Pickard et al., published last week in PNAS. The idea: treat the human body like a complex dynamical system (which, inconveniently, it is) and use mathematical “observability theory” to identify which few biomarkers tell you the most about what’s going on inside.
In plain terms, it’s a framework for choosing the right test points in a living system. Instead of wiring an oscilloscope to a circuit board, you’re “probing” gene expression, neural signals, or metabolic markers, and asking: Which measurements let me reconstruct the full picture?
The team behind this approach applied it across everything from bacterial genes to human brainwaves. In some experiments, the method could estimate unmeasured genes with about 50% error — impressive, considering biology’s noise makes Wi-Fi in a storm look stable. In brain studies, the algorithm even revealed that some EEG electrodes are basically freeloaders, contributing little to understanding what the neurons are up to. (So yes, even your neurons have that one coworker who never pulls their weight.)
The broader vision is seductive: "A medical system that diagnoses itself dynamically", focusing only on the sensors that matter most at a given moment. Imagine wearable devices that don’t just collect endless data, but decide in real time which data is most informative - sparing us from both data fatigue and unnecessary costs.
It’s also a philosophical pivot: biology isn’t static. The “best” biomarker today might be irrelevant tomorrow, just as a stable circuit becomes unpredictable when the current spikes. Medicine, for all its imaging and sequencing power, still operates like a lab tech armed with every tool but no schematic. Pickard’s framework offers that missing circuit diagram.
So next time you’re overwhelmed by medical testing options, remember - the goal isn’t to measure everything, it’s to measure wisely. In the coming era of dynamic biomarkers, your body might finally come with its own built-in diagnostic dashboard.
And who knows? Someday your doctor’s favorite prescription might be:
> “Let’s check your observability matrix.”
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