Integrating Artificial Intelligence Tools in the Clinical Research Setting: The Ovarian Cancer Use Case.
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Embargo End Date
ICR Authors
Authors
Escudero Sanchez, L
Buddenkotte, T
Al Sa'd, M
McCague, C
Darcy, J
Rundo, L
Samoshkin, A
Graves, MJ
Hollamby, V
Browne, P
Crispin-Ortuzar, M
Woitek, R
Sala, E
Schönlieb, C-B
Doran, SJ
Öktem, O
Buddenkotte, T
Al Sa'd, M
McCague, C
Darcy, J
Rundo, L
Samoshkin, A
Graves, MJ
Hollamby, V
Browne, P
Crispin-Ortuzar, M
Woitek, R
Sala, E
Schönlieb, C-B
Doran, SJ
Öktem, O
Document Type
Journal Article
Date
2023-08-30
Date Accepted
2023-08-22
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.
Citation
Diagnostics, 2023, 13 (17), pp. 2813 -
Source Title
Diagnostics
Publisher
MDPI
ISSN
2075-4418
eISSN
2075-4418
2075-4418
2075-4418
Collections
Research Team
Magnetic Resonance