Cybersecurity Maturity and Risk Profiling of AI-Enabled Medical Sensors: A Cross-Manufacturer Comparative Analysis

Abstract

The rapid integration of artificial intelligence (AI) into modern medical sensors—from multi-parameter hospital patches to ambulatory wearables, implantable cardiac devices, and continuous glucose monitoring (CGM)—is transforming clinical diagnostics and patient monitoring. Still, it introduces complex cybersecurity risks spanning device firmware, wireless communication, cloud infrastructure, and AI models. This study proposes a new integrated methodology for assessing cybersecurity maturity in AI-enhanced medical sensors based on ISO 14971, NIST 800-30/53, and AHP weighting. Using ten key criteria, it enables objective comparison among leading manufacturers and generates two indicators: a weighted security score (WSS) and a risk profile score (RPS). The study reveals critical vulnerabilities in the communication security and AI modules of specific devices, and greater cryptographic resilience in implantable systems. The results have direct practical value for engineers, clinicians, and organizations that implement such devices, as well as for educating students in medical technology and cybersecurity. The publication also highlights the need to develop unified AI-oriented security standards throughout the life cycle of medical sensor devices.