Clinical Safety Assessment of Biosensor-Integrated Devices

Abstract

Biosensor-integrated medical devices -- encompassing continuous glucose monitors, implantable cardiac monitors with haemodynamic sensors, closed-loop neurostimulators, and wearable multi-analyte platforms -- generate real-time physiological data that drives therapeutic decisions, creating a unique safety paradigm where sensor inaccuracy, signal artefacts, and algorithm misinterpretation can directly cause patient harm through inappropriate treatment modification. Unlike standalone sensors where measurement error produces only informational risk, biosensor-integrated devices operate in closed or semi-closed loops where erroneous readings trigger drug delivery adjustments, stimulation parameter changes, or clinical alerts with immediate physiological consequences. This study presents the Biosensor-Integrated Device Safety Assessment Framework (BIDSAF), evaluating five safety assessment methodologies -- conventional biocompatibility testing per ISO 10993, sensor analytical performance validation, closed-loop hazard analysis, clinical outcome-based safety evaluation, and AI-enhanced real-time safety monitoring - across four biosensor device categories: continuous glucose monitors (CGMs), implantable haemodynamic sensors, adaptive neurostimulators, and wearable multi-analyte platforms. Our Biosensor Device Safety Score (BDSS) integrates biocompatibility adequacy, analytical accuracy margins, closed-loop failure mode coverage, clinical adverse event rates, and real-time monitoring comprehensiveness. AI-enhanced real-time safety monitoring achieved the highest BDSS (0.924) through anomaly detection algorithms that identified 94% of sensor degradation events before clinically significant measurement drift occurred, while closed-loop hazard analysis achieved the highest failure mode coverage (0.958) through systematic fault tree analysis of 842 sensor-algorithm-actuator interaction pathways.