Validation of Artificial Intelligence Containing Products Across the Regulated Healthcare Industries.

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, is 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 software validation, including 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.

Machine Learning for Health: Algorithm Auditing & Quality Control.

Developers proposing new machine learning for health (ML4H) tools often pledge to match or even surpass the performance of existing tools, yet the reality is usually more complicated. Reliable deployment of ML4H to the real world is challenging as examples from diabetic retinopathy or Covid-19 screening show. We envision an integrated framework of algorithm auditing and quality control that provides a path towards the effective and reliable application of ML systems in healthcare. In this editorial, we give a summary of ongoing work towards that vision and announce a call for participation to the special issue  Machine Learning for Health: Algorithm Auditing & Quality Control in this journal to advance the practice of ML4H auditing.

Secondary use of clinical data in healthcare providers - an overview on research, regulatory and ethical requirements.

Hospital providers, physicians and researchers are interested in a cross-institutional use of their data for clinical research. This interest has led to the question whether the scientific potential of the data stored in so many different systems can be unfolded by the establishment of a cross-institutional medical data warehouse. The aim of this paper is to describe the ethical and regulatory requirements and to develop a solution architecture considering technical and organisational aspects. The present paper uses a structured approach to collect user requirements. The requirements are discussed with legal experts. The work was complemented by extended literature research. An essential requirement is the cross-institutional merging of the data. Here, aspects of data protection as the informed consent, or transparency must be considered. In addition it is essential to protect the researchers through transparency from accusations on publication bias. Technical and organisational solutions in combination of data protection, and data security enable an operation of a central medical data warehouse in compliance with the law. The usage of this infrastructure for research can contribute to an improvement of the treatment quality, and patient safety if there is an appropriate transparency. This contributes to innovation and added value of a hospital group.