Is the FDA regulation of cardiology AI devices supporting cardiovascular innovation: a scoping review

Abstract

BACKGROUND: Artificial intelligence (AI) and machine learning (ML) have shown immense potential in cardiology, leveraging data-driven insights to enhance diagnosis, treatment planning and patient care. This study presents a comprehensive evaluation of US Food and Drug Administration (FDA)-approved AI/ML devices in cardiology, analysing trends in clinical applications, regulatory pathways and evidence transparency. METHODS: FDA clearance summaries from the AI/ML medical device database were reviewed to identify cardiology-specific applications. Devices were categorised using the descriptive, diagnostic, predictive and prescriptive framework. Regulatory pathways, AI technologies and validation data were critically assessed. RESULTS: Of 1016 FDA-approved AI/ML devices, 277 (27.3%) had cardiology applications, predominantly for imaging (65.3%) and diagnostics (64.3%). Predictive and prescriptive tools constituted only 5.4% and 0.7%, respectively. Most devices (97.1%) were cleared via the 510(k) pathway, with 58.0% at risk of predicate creep. Quality of clinical evidence was limited, with only 3.2% of devices supported by high-quality trials. The type of AI technology was often underreported (58.8%). CONCLUSION: While AI/ML technologies are reshaping cardiology, regulatory challenges and reporting transparency impede their optimal use. Strengthened regulatory frameworks, improved trial design and robust post-market surveillance are essential to ensure safety, efficacy and equity in the deployment of AI tools in cardiology.

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