A distributed approach to the regulation of clinical AI.
expert_opinion · Level V
Where this comes from
- Record sourced from PubMed, PMID 36812520.
- Also identified by DOI 10.1371/journal.pdig.0000040 and PMC identifier 9931237.
- 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
Regulation is necessary to ensure the safety, efficacy and equitable impact of clinical artificial intelligence (AI). The number of applications of clinical AI is increasing, which, amplified by the need for adaptations to account for the heterogeneity of local health systems and inevitable data drift, creates a fundamental challenge for regulators. Our opinion is that, at scale, the incumbent model of centralized regulation of clinical AI will not ensure the safety, efficacy, and equity of implemented systems. We propose a hybrid model of regulation, where centralized regulation would only be required for applications of clinical AI where the inference is entirely automated without clinician review, have a high potential to negatively impact the health of patients and for algorithms that are to be applied at national scale by design. This amalgam of centralized and decentralized regulation we refer to as a distributed approach to the regulation of clinical AI and highlight the benefits as well as the pre-requisites and challenges involved.