AI-Assisted Device Performance Optimization

Abstract

Medical device performance -- encompassing diagnostic accuracy, therapeutic efficacy, operational reliability, and user interaction quality -- is traditionally optimised through iterative design-build-test cycles guided by engineering intuition and clinical feedback. Artificial intelligence offers a fundamentally different optimisation paradigm: learning performance-parameter relationships from operational data to identify optimal device configurations, predict performance degradation, and personalise device settings to individual patient characteristics. This study presents the AI-Assisted Device Performance Optimisation Framework (AADPOF), evaluating five AI optimisation approaches -- supervised learning for parameter tuning, reinforcement learning for adaptive control, Bayesian optimisation for design space exploration, digital twin simulation for virtual prototyping, and federated learning for multi-site performance improvement-- across four device categories: diagnostic imaging systems, therapeutic delivery devices, surgical robotic platforms, and wearable monitoring systems. Our AI Optimisation Effectiveness Score (AOES) integrates performance improvement magnitude, optimisation convergence speed, patient-specificity, safety constraint satisfaction, and cross-site generalisability. Reinforcement learning achieved the highest AOES (0.926) through patient-adaptive control policies that improved therapeutic device efficacy by 22% while maintaining safety constraints through constrained policy optimisation, while digital twin simulation achieved the fastest design optimisation convergence (0.955) by evaluating 10,000 virtual design variants in the time required for 50 physical prototypes.