The absence of full lifecycle risk management for AI-based medical devices in radiology

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Authors: Jia Li, Zicong Guo, Yi Guo, Rui Xiao, Yuxiao Ding, Wenjie Shi, Wei Liu
Cited by: 1
References: 30

Abstract

This study represents the systematic examination of full lifecycle management for radiology artificial intelligence medical (AI) devices approved by the US Food and Drug Administration (FDA), with an in-depth analysis of post-market adverse events, recalls, and software update patterns. An analysis of 956 radiology AI medical devices approved by the FDA between September 1995 and September 2025 revealed that 429 (44.87%) of these devices had undergone software version updates. Adverse events related to software defects involved 15 products (34.88%); among these, 4 products (26.67%) underwent version updates, and for 3 products (20%), the companies initiated recalls following the occurrence of adverse events. There were a total of 124 reports (68.13%) of product recalls caused by software defects; for 38 of these reports (30.65%), corresponding to 8 products, the manufacturers corrected the defects by updating the software after implementing the recall. We make three contributions: (1) identifying that the vast majority of companies lack a closed-loop risk management system encompassing adverse events, recalls, and software updates, (2) analysing the frequency and characteristics of software updates for radiology equipment, and (3) exploring the critical role of full lifecycle management in the regulation of AI-based medical devices.

Fields of Study & Subjects

Artificial Intelligence in Healthcare and Education Quality and Safety in Healthcare Explainable Artificial Intelligence (XAI)

Publication details

Journal / source
npj Digital Medicine
Publisher
Springer Science and Business Media LLC
Volume
9
Issue
1
Publication date
4 May 2026
Language
EN