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Integrating Artificial Intelligence Tools in the Clinical Research Setting: The Ovarian Cancer Use Case
Univ Cambridge, Dept Radiol, Cambridge CB2 0QQ, England.;Li Ka Shing Ctr, Canc Res UK Cambridge Ctr, Cambridge CB2 0RE, England.;Natl Canc Imaging Translat Accelerator NCITA Conso, London, England..
Univ Cambridge, Dept Radiol, Cambridge CB2 0QQ, England.;Univ Cambridge, Dept Appl Math & Theoret Phys, Wilberforce Rd, Cambridge CB3 0WA, England.;Univ Hosp Hamburg Eppendorf, Dept Diagnost & Intervent Radiol & Nucl Med, D-20246 Hamburg, Germany.;Jung Diagnost GmbH, D-22335 Hamburg, Germany..
Natl Canc Imaging Translat Accelerator NCITA Conso, London, England.;Imperial Coll, Canc Imaging Ctr, Dept Surg & Canc, London SW7 2AZ, England..
Univ Cambridge, Dept Radiol, Cambridge CB2 0QQ, England.;Li Ka Shing Ctr, Canc Res UK Cambridge Ctr, Cambridge CB2 0RE, England.;Cambridge Univ Hosp NHS Fdn Trust, Cambridge CB2 0QQ, England..
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2023 (English)In: Diagnostics, ISSN 2075-4418, Vol. 13, no 17, article id 2813Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
MDPI , 2023. Vol. 13, no 17, article id 2813
Keywords [en]
artificial intelligence, cancer research, imaging, clinical integration, radiomics
National Category
Computer Sciences Radiology, Nuclear Medicine and Medical Imaging
Identifiers
URN: urn:nbn:se:kth:diva-337027DOI: 10.3390/diagnostics13172813ISI: 001061016200001PubMedID: 37685352Scopus ID: 2-s2.0-85170386154OAI: oai:DiVA.org:kth-337027DiVA, id: diva2:1799613
Note

QC 20230922

Available from: 2023-09-22 Created: 2023-09-22 Last updated: 2023-09-22Bibliographically approved

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Öktem, Ozan

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