The METRIC-framework for assessing data quality for trustworthy AI in medicine: a systematic review

arXiv:2402.13635v1 Announce Type: new
Abstract: The adoption of machine learning (ML) and, more specifically, deep learning (DL) applications into all major areas of our lives is underway. The development of trustworthy AI is especially important in medicine due to the large implications for patients' lives. While trustworthiness concerns various aspects including ethical, technical and privacy requirements, we focus on the importance of data quality (training/test) in DL. Since data quality dictates the behaviour of ML products, evaluating data quality will …

abstract adoption ai in medicine applications arxiv cs.ai cs.lg data data quality deep learning development framework machine machine learning major medicine patients quality review trustworthy trustworthy ai type