Distributed Approach’ to Clinical AI Regulation

Public health researchers argue that the centralized regulation of artificial intelligence at the national level is not sufficient for safety, efficacy, and equity.

Researchers argue that the national, centralized regulation of clinical artificial intelligence (AI) is not sufficient and instead propose a hybrid model of centralized and decentralized regulation.

In an opinion piece published in PLOS Digital Health, public health researchers at Harvard note that the increase in clinical AI applications, combined with the need to adapt applications to account for differences between local health systems, creates a significant challenge for regulators.

Currently, the US Food and Drug Administration (FDA) regulates clinical AI under the classification of software-based medical devices. Medical device approval is typically obtained via premarket clearance, de novo classification, or premarket approval. In practice, this usually involves the approval of a “static” model, meaning that any change in data, algorithm, or intended use after initial approval requires reapplication for approval. To receive approval, developers must demonstrate a model’s performance on an appropriately heterogeneous dataset.

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