Clinicians depend on continuous patient data to make life-critical decisions, yet as sensors age or are exposed to certain conditions, their readings can become unreliable.

The dilemma is stark: recalibration is disruptive and often goes unnoticed, but waiting for clear failure signals risks harm.

This silent degradation blurs the line between actionable data and dangerous noise, leaving healthcare staff in a constant state of uncertainty.

Existing hardware lacks self-diagnosing capabilities to detect early-stage sensor drift, and current calibration protocols are infrequent or manual, which can introduce human error or be overlooked amid high workloads.

There is no reliable alert system for gradual sensor inaccuracy.

Periodic manual recalibration and routine device swaps are standard but disruptive, costly, and prone to oversight.

Some software-based corrections exist but rarely address physical drift in hardware.