Researchers unveiled a new taxonomy and online tool for classifying 1,000+ FDA-cleared AI/ML-enabled medical devices, with radiology as the dominant specialty.
Key Details
- 1A database and online tool summarize 1,016 FDA-cleared AI/ML devices through December 20, 2024.
- 2736 unique devices were identified after consolidating multiple authorizations per device.
- 3Most devices (88.2%) are built for radiology; neurology (2.9%) and hematology (1.9%) follow.
- 4Clinical function analysis: 84.1% assist in assessment, not intervention; 85.6% of AI functions are analysis, mostly for quantification/feature localization (65%), and triage (12.9%).
- 5Only three devices use EHR/tabular data, showing clear dominance of imaging data inputs.
- 6Key application types, definitions, and usage patterns are detailed in the report and a public website.
Why It Matters
This resource offers unprecedented clarity about the distribution, function, and data types of AI radiology devices, supporting better validation strategies and more informed clinical adoption. It empowers radiology professionals, vendors, and regulators to understand which AI tools exist, what they actually do, and where gaps remain.
Source
AuntMinnie
Related News
•AuntMinnie
AI Model Uses Ultrasound to Assess Fetal Lung Maturity
Researchers demonstrated an AI model's strong accuracy in measuring fetal lung maturity from ultrasound images.
•AuntMinnie
AI Model Predicts Dosimetry for Lu-177 PSMA Therapy Using PET/CT
A machine learning PET/CT model shows promise for predicting radiation dose prior to Lu-177 PSMA therapy in prostate cancer patients.
•AuntMinnie
AI Advances in Ultrasound Highlighted at AIUM 2026 Keynote
AI is increasingly enhancing ultrasound imaging, clinical workflows, and education, though challenges in trust and implementation remain.