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Safety Justification of Healthcare Applications Using Synthetic Datasets
George Despotou, Stuart Harrison, Sean White, Theodoros N. Arvanitis
35 - 38
10.3233/SHTI200487
Research Article
Studies in Health Technology and Informatics
Volume 272: The Importance of Health Informatics in Public Health during a Pandemic
Abstract

Background:

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.

Objective:

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.

Method:

Assurance case argument development approaches were used, including high level data related risk identification.

Result:

An outline of the justification of such applications, focusing on the contribution of the RSD.

Conclusions:

Use of RSD will require specific arguments and evidence, which will affect the adopted methods. Mutually supporting arguments can result in a compelling justification.

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