AI-Based Risk Assessment in Medical Device Design

Abstract

Medical device risk assessment -- the systematic identification, evaluation, and mitigation of hazards throughout the device lifecycle -- is mandated by ISO 14971 and enforced by regulatory authorities (FDA, EU MDR) as a prerequisite for market authorisation. Traditional risk assessment relies on expert-driven failure mode and effects analysis (FMEA), fault tree analysis (FTA), and hazard analysis, which are labour-intensive, subjective, and often incomplete: studies show that conventional FMEA identifies only 40-60% of failure modes that eventually manifest in clinical use. AI-based approaches can augment risk assessment by mining adverse event databases (MAUDE, MHRA), predicting failure modes from design specifications, estimating risk severity from clinical outcome data, and continuously monitoring post-market safety signals. We present the AI Risk Assessment for Medical Devices Framework (AIRAMDF), evaluating five AI approaches-- NLP-based adverse event mining, predictive failure mode analysis, Bayesian risk quantification, digital twin risk simulation, and continuous post-market surveillance AI -- across four medical device categories (cardiovascular implants, orthopaedic prostheses, diagnostic imaging systems, and wearable therapeutic devices). Our AI Risk Assessment Score (AIRAS) measures hazard identification completeness, risk quantification accuracy, predictive lead time, regulatory alignment, and actionability. NLP-based adverse event mining achieves the highest AIRAS (0.926) through comprehensive extraction of failure modes from 8 million MAUDE reports, while digital twin risk simulation achieves the highest predictive capability (0.960) through physics-based prediction of failure before clinical manifestation.