Perspectives on Assurance Case Development for Retinal Disease Diagnosis Using Deep Learning

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We report our experience with developing an assurance case for a deep learning system used for retinal disease diagnosis and referral. We investigate how an assurance case could clarify the scope and structure of the primary argument and identify sources of uncertainty. We also explore the need for an assurance argument pattern that could provide developers with a reusable template for communicating and structuring the different claims and evidence and clarifying the clinical context rather than merely focusing on meeting or exceeding performance measures.

Original languageEnglish
Title of host publicationAIME 2019: Artificial Intelligence in Medicine
PublisherSpringer
Pages365-370
ISBN (Print)978-3-030-21641-2
DOIs
Publication statusPublished - Jun 2019

Publication series

NameLecture Notes in Artificial Intelligence
PublisherSpringer
Volume11526
ISSN (Print)0302-9743

Bibliographical note

© Springer Nature Switzerland AG 2019. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details.

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