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Recommendations for the use of pediatric data in artificial intelligence and machine learning ACCEPT-AI
V Muralidharan1, A Burgart2, R Daneshjou3,4
1Department of Dermatology, Stanford University, Stanford, USA. vmurali5@stanford.edu.
Insights
ACCEPT-AI provides recommendations for safely using pediatric data in artificial intelligence (AI) and machine learning (ML) research. This framework ensures ethical considerations like age, consent, and data protection are prioritized for children in AI.
Area of Science:
- Pediatric research ethics
- Artificial intelligence and machine learning
Background:
- Ethical considerations are paramount when incorporating pediatric data into AI/ML research.
- Existing guidelines may not fully address the unique challenges of pediatric data in AI.
Purpose of the Study:
- To introduce ACCEPT-AI, a novel framework for the ethical and safe use of pediatric data in AI/ML.
- To provide comprehensive recommendations guiding researchers, clinicians, and policymakers.
Main Methods:
- Development based on fundamental ethical principles of pediatric and AI research.
- Incorporation of key considerations: age, consent, assent, communication, equity, data protection, and technology.
Main Results:
- ACCEPT-AI offers a structured approach to ethical AI/ML research involving children.
- The framework addresses critical aspects from data collection to technological implementation.
Conclusions:
- ACCEPT-AI serves as a vital tool for ensuring responsible innovation in pediatric AI/ML.
- It can be used independently or alongside existing AI/ML guidelines to enhance child data protection.
Abstract:
ACCEPT-AI is a framework of recommendations for the safe inclusion of pediatric data in artificial intelligence and machine learning (AI/ML) research. It has been built on fundamental ethical principles of pediatric and AI research and incorporates age, consent, assent, communication, equity, protection of data, and technological considerations. ACCEPT-AI has been designed to guide researchers, clinicians, regulators, and policymakers and can be utilized as an independent tool, or adjunctively to existing AI/ML guidelines.
