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Integrating Artificial Intelligence Tools in the Clinical Research Setting: The Ovarian Cancer Use Case.

Accepted version
Peer-reviewed

Type

Article

Change log

Authors

Escudero Sanchez, Lorena  ORCID logo  https://orcid.org/0000-0003-3464-9206
Buddenkotte, Thomas  ORCID logo  https://orcid.org/0000-0003-4872-4246
Al Sa'd, Mohammad 
Darcy, James 

Abstract

Artificial intelligence (AI) methods applied to healthcare problems have shown enormous potential to alleviate the burden of health services worldwide and to improve the accuracy and reproducibility of predictions. In particular, developments in computer vision are creating a paradigm shift in the analysis of radiological images, where AI tools are already capable of automatically detecting and precisely delineating tumours. However, such tools are generally developed in technical departments that continue to be siloed from where the real benefit would be achieved with their usage. Significant effort still needs to be made to make these advancements available, first in academic clinical research and ultimately in the clinical setting. In this paper, we demonstrate a prototype pipeline based entirely on open-source software and free of cost to bridge this gap, simplifying the integration of tools and models developed within the AI community into the clinical research setting, ensuring an accessible platform with visualisation applications that allow end-users such as radiologists to view and interact with the outcome of these AI tools.

Description

Keywords

artificial intelligence, cancer research, clinical integration, imaging, radiomics

Journal Title

Diagnostics (Basel)

Conference Name

Journal ISSN

2075-4418
2075-4418

Volume Title

Publisher

MDPI AG
Sponsorship
Wellcome Trust (215733/Z/19/Z)
National Institute for Health and Care Research (IS-BRC-1215-20014)