Validation of artificial intelligence containing products across the regulated healthcare industries

TL;DR

This paper compares validation approaches for AI/ML products across regulated healthcare sectors, aiming to standardize terminology and methodologies to enhance collaboration and streamline product development.

Contribution

It introduces a common framework and terminology for validating AI-containing healthcare products across pharmaceutical, medical device, and diagnostics industries.

Findings

Distinction between broad and narrow validation approaches.

Introduction to primary validation methodologies for AI software.

Perspectives on compliant AI development in pharma and medical devices.

Abstract

Purpose: The introduction of artificial intelligence / machine learning (AI/ML) products to the regulated fields of pharmaceutical research and development (R&D) and drug manufacture, and medical devices (MD) and in-vitro diagnostics (IVD), poses new regulatory problems: a lack of a common terminology and understanding leads to confusion, delays and product failures. Validation as a key step in product development, common to each of these sectors including computerized systems and AI/ML development, offers an opportune point of comparison for aligning people and processes for cross-sectoral product development. Methods: A comparative approach, built upon workshops and a subsequent written sequence of exchanges, summarized in a look-up table suitable for mixed-teams work. Results: 1. A bottom-up, definitions led, approach which leads to a distinction between broad vs narrow validation, and their relationship to regulatory regimes. 2. Common basis introduction to the primary methodologies for AI-containing software validation. 3. Pharmaceutical drug development and MD/IVD specific perspectives on compliant AI software development, as a basis for collaboration. Conclusions: Alignment of the terms and methodologies used in validation of software products containing artificial intelligence / machine learning (AI/ML) components across the regulated industries of human health is a vital first step in streamlining processes and improving workflows.