Research finds insufficient data transparency for FDA-cleared AI devices in Alzheimer’s imaging, raising bias and generalizability concerns.
Key Details
- 124 AI/ML-based devices for Alzheimer's disease and related dementias (ADRD) were FDA-authorized between January 2015 and December 2024.
- 291.7% (22/24) were cleared via the 510(k) pathway; 8.3% (2/24) through de novo classification.
- 3For 12 devices, there was no info on training or validation data in FDA summaries or peer-reviewed articles.
- 4Only 2 FDA summaries (8.3%) included external validation data; 10 peer-reviewed articles (41.7%) provided such data.
- 5Reporting of disease status, age, sex, race, and ethnicity was rare across most devices; demographic transparency was lacking.
- 6Most devices (21) were reviewed by radiology panels and primarily involved MRI and PET imaging.
Why It Matters
Transparent disclosure of training and validation data, especially demographic details, is vital for assessing bias and clinical accuracy in AI-based imaging devices. Without sufficient data transparency, there is uncertainty about the generalizability and safety of these AI tools in real-world, diverse patient populations.
Source
AuntMinnie
Related News
•Radiology Business
AI-Powered Tool Streamlines CT Scan Prioritization in Emergency Departments
An AI-based CT queue system significantly reduces wait times for ED patients by prioritizing scans likely to reveal critical findings.
•Radiology Business
AI Workflow Enables General Radiologists to Match Breast Specialists in Screening
AI-powered workflow helps generalist radiologists detect breast cancer at rates comparable to specialists.
•Radiology Business
LLMs Automate Radiology Report Quality Control, Study Finds
LLM-based systems can rapidly automate radiology report quality control, saving significant manual review time.