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
•AuntMinnie
Radiology Receives Declining Share of Industry Research Funding
Radiologists received only 1.1% of industry-funded research payments in 2024, with a continuing downward trend.
•AuntMinnie
GPT-4o AI Matches Radiologists in Follow-Up Imaging Recommendations
GPT-4o matched the performance of experienced radiologists and surpassed residents in recommending follow-up imaging from routine radiology reports.
•Cardiovascular Business
AI Leverages Head CTs for Automated Heart Risk Assessments
AI models can turn routine head CT scans into automated cardiovascular risk assessments, expanding the utility of radiology studies.