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A trustworthy AI reality-check: the lack of transparency of artificial intelligence products in healthcare.
- Source :
- Frontiers in Digital Health; 2024, p1-11, 11p
- Publication Year :
- 2024
-
Abstract
- Trustworthy medical AI requires transparency about the development and testing of underlying algorithms to identify biases and communicate potential risks of harm. Abundant guidance exists on how to achieve transparency for medical AI products, but it is unclear whether publicly available information adequately informs about their risks. To assess this, we retrieved public documentation on the 14 available CE-certified AI-based radiology products of the II b risk category in the EU from vendor websites, scientific publications, and the European EUDAMED database. Using a self-designed survey, we reported on their development, validation, ethical considerations, and deployment caveats, according to trustworthy AI guidelines. We scored each question with either 0, 0.5, or 1, to rate if the required information was "unavailable", "partially available," or "fully available." The transparency of each product was calculated relative to all 55 questions. Transparency scores ranged from 6.4% to 60.9%, with a median of 29.1%. Major transparency gaps included missing documentation on training data, ethical considerations, and limitations for deployment. Ethical aspects like consent, safety monitoring, and GDPR-compliance were rarely documented. Furthermore, deployment caveats for different demographics and medical settings were scarce. In conclusion, public documentation of authorized medical AI products in Europe lacks sufficient public transparency to inform about safety and risks. We call on lawmakers and regulators to establish legally mandated requirements for public and substantive transparency to fulfill the promise of trustworthy AI for health. [ABSTRACT FROM AUTHOR]
- Subjects :
- ARTIFICIAL intelligence laws
DOCUMENTATION
HEALTH services administration
WORLD Wide Web
SERIAL publications
RESEARCH funding
COMPUTER software
RADIOLOGIC technology
ARTIFICIAL intelligence
PRODUCT design
DESCRIPTIVE statistics
MEDICAL equipment laws
REGULATORY approval
MACHINE learning
QUALITY assurance
ACCESS to information
MEDICAL equipment safety measures
ALGORITHMS
Subjects
Details
- Language :
- English
- Database :
- Complementary Index
- Journal :
- Frontiers in Digital Health
- Publication Type :
- Academic Journal
- Accession number :
- 175955168
- Full Text :
- https://doi.org/10.3389/fdgth.2024.1267290