Author:
Marti-Bonmati Luis,Koh Dow-Mu,Riklund Katrine,Bobowicz Maciej,Roussakis Yiannis,Vilanova Joan C.,Fütterer Jurgen J.,Rimola Jordi,Mallol Pedro,Ribas Gloria,Miguel Ana,Tsiknakis Manolis,Lekadir Karim,Tsakou Gianna
Abstract
AbstractTo achieve clinical impact in daily oncological practice, emerging AI-based cancer imaging research needs to have clearly defined medical focus, AI methods, and outcomes to be estimated. AI-supported cancer imaging should predict major relevant clinical endpoints, aiming to extract associations and draw inferences in a fair, robust, and trustworthy way. AI-assisted solutions as medical devices, developed using multicenter heterogeneous datasets, should be targeted to have an impact on the clinical care pathway. When designing an AI-based research study in oncologic imaging, ensuring clinical impact in AI solutions requires careful consideration of key aspects, including target population selection, sample size definition, standards, and common data elements utilization, balanced dataset splitting, appropriate validation methodology, adequate ground truth, and careful selection of clinical endpoints. Endpoints may be pathology hallmarks, disease behavior, treatment response, or patient prognosis. Ensuring ethical, safety, and privacy considerations are also mandatory before clinical validation is performed. The Artificial Intelligence for Health Imaging (AI4HI) Clinical Working Group has discussed and present in this paper some indicative Machine Learning (ML) enabled decision-support solutions currently under research in the AI4HI projects, as well as the main considerations and requirements that AI solutions should have from a clinical perspective, which can be adopted into clinical practice. If effectively designed, implemented, and validated, cancer imaging AI-supported tools will have the potential to revolutionize the field of precision medicine in oncology.
Funder
Horizon 2020 Framework Programme
Publisher
Springer Science and Business Media LLC
Subject
Radiology, Nuclear Medicine and imaging
Cited by
10 articles.
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