Safety Justification of Healthcare Applications Using Synthetic Datasets.

Abstract

Increasing numbers of intelligent healthcare applications are developed by analysing big data, on which they are trained. It is necessary to assure that such applications will be safe for patients; this entails validation against datasets. But datasets cannot be shared easily, due to privacy, and consent issues, resulting in delaying innovation. Realistic Synthetic Datasets (RSDs), equivalent to the real datasets, are seen as a solution to this. To develop the outline for safety justification of an application, validated with an RSD, and identify the safety evidence the RSD developers will need to generate. Assurance case argument development approaches were used, including high level data related risk identification. An outline of the justification of such applications, focusing on the contribution of the RSD. Use of RSD will require specific arguments and evidence, which will affect the adopted methods. Mutually supporting arguments can result in a compelling justification.