Generalization-a key challenge for responsible AI in patient-facing clinical applications.
review · Level V
Where this comes from
- Record sourced from PubMed, PMID 38773304.
- Also identified by DOI 10.1038/s41746-024-01127-3 and PMC identifier 11109198.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
Generalization – the ability of AI systems to apply and/or extrapolate their knowledge to new data which might differ from the original training data – is a major challenge for the effective and responsible implementation of human-centric AI applications. Current debate in bioethics proposes selective prediction as a solution. Here we explore data-based reasons for generalization challenges and look at how selective predictions might be implemented technically, focusing on clinical AI applications in real-world healthcare settings.