A framework for validating AI in precision medicine: considerations from the European ITFoC consortium

dc.contributor.authorTsopra, Rosy
dc.contributor.authorFernandez, Xose
dc.contributor.authorLuchinat, Claudio
dc.contributor.authorAlberghina, Lilia
dc.contributor.authorLehrach, Hans
dc.contributor.authorVanoni, Marco
dc.contributor.authorDreher, Felix
dc.contributor.authorSezerman, O. Ugur
dc.contributor.authorCuggia, Marc
dc.contributor.authorde Tayrac, Marie
dc.contributor.authorMiklasevics, Edvins
dc.contributor.authorItu, Lucian Mihai
dc.contributor.authorGeanta, Marius
dc.contributor.authorOgilvie, Lesley
dc.contributor.authorGodey, Florence
dc.contributor.authorBoldisor, Cristian Nicolae
dc.contributor.authorCampillo-Gimenez, Boris
dc.contributor.authorCioroboiu, Cosmina
dc.contributor.authorCiusdel, Costin Florian
dc.contributor.authorComan, Simona
dc.contributor.authorCubelos, Oliver Hijano
dc.contributor.authorItu, Alina
dc.contributor.authorLange, Bodo
dc.contributor.authorLe Gallo, Matthieu
dc.contributor.authorLespagnol, Alexandra
dc.contributor.authorMauri, Giancarlo
dc.contributor.authorSoykam, H. Okan
dc.contributor.authorRance, Bastien
dc.contributor.authorTurano, Paola
dc.contributor.authorTenori, Leonardo
dc.contributor.authorVignoli, Alessia
dc.contributor.authorWierling, Christoph
dc.contributor.authorBenhabiles, Nora
dc.contributor.authorBurgun, Anita
dc.date.accessioned2023-02-21T12:42:17Z
dc.date.available2023-02-21T12:42:17Z
dc.date.issued2021-01-01
dc.description.abstractBackground Artificial intelligence (AI) has the potential to transform our healthcare systems significantly. New AI technologies based on machine learning approaches should play a key role in clinical decision-making in the future. However, their implementation in health care settings remains limited, mostly due to a lack of robust validation procedures. There is a need to develop reliable assessment frameworks for the clinical validation of AI. We present here an approach for assessing AI for predicting treatment response in triple-negative breast cancer (TNBC), using real-world data and molecular -omics data from clinical data warehouses and biobanks. Methods The European ``ITFoC (Information Technology for the Future Of Cancer){''} consortium designed a framework for the clinical validation of AI technologies for predicting treatment response in oncology. Results This framework is based on seven key steps specifying: (1) the intended use of AI, (2) the target population, (3) the timing of AI evaluation, (4) the datasets used for evaluation, (5) the procedures used for ensuring data safety (including data quality, privacy and security), (6) the metrics used for measuring performance, and (7) the procedures used to ensure that the AI is explainable. This framework forms the basis of a validation platform that we are building for the ``ITFoC Challenge{''}. This community-wide competition will make it possible to assess and compare AI algorithms for predicting the response to TNBC treatments with external real-world datasets. Conclusions The predictive performance and safety of AI technologies must be assessed in a robust, unbiased and transparent manner before their implementation in healthcare settings. We believe that the consideration of the ITFoC consortium will contribute to the safe transfer and implementation of AI in clinical settings, in the context of precision oncology and personalized care.
dc.description.issue1
dc.description.issueOCT 2
dc.description.volume21
dc.identifier.doi10.1186/s12911-021-01634-3
dc.identifier.urihttps://hdl.handle.net/11443/2801
dc.identifier.urihttp://dx.doi.org/10.1186/s12911-021-01634-3
dc.identifier.wosWOS:000702775200001
dc.publisherBMC
dc.relation.ispartofBMC MEDICAL INFORMATICS AND DECISION MAKING
dc.subjectArtificial intelligence
dc.subjectPrecision medicine
dc.subjectPersonalized medicine
dc.subjectComputerized decision support systems
dc.subjectCancer
dc.subjectOncology
dc.titleA framework for validating AI in precision medicine: considerations from the European ITFoC consortium
dc.typeArticle
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