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

Rosy Tsopra 1, 2, 3, 4, 5 Xose Fernandez 6 Claudio Luchinat 7 Lilia Alberghina 8 Hans Lehrach 9 Marco Vanoni 8 Felix Dreher 10 O Sezerman 11 Marc Cuggia 4, 5 Marie de Tayrac 12, 5 Edvins Miklasevics 13 Lucian Itu 14 Marius Geanta 15 Lesley Ogilvie 9 Florence Godey 16, 17, 18 Cristian Boldisor 14 Boris Campillo-Gimenez 4, 17 Cosmina Cioroboiu 15 Costin Ciusdel 14 Simona Coman 14 Oliver Hijano Cubelos 6 Alina Itu 14 Bodo Lange 10 Matthieu Le Gallo 16, 18 Alexandra Lespagnol 5 Giancarlo Mauri 8 H Soykam 19 Bastien Rance 1, 2, 3 Paola Turano 7 Leonardo Tenori 7 Alessia Vignoli 7 Christoph Wierling 10 Nora Benhabiles 20, 21 Anita Burgun 1, 2, 3
Abstract : Background: 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.
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Submitted on : Monday, October 11, 2021 - 3:40:29 PM
Last modification on : Thursday, January 20, 2022 - 5:29:21 PM
Long-term archiving on: : Wednesday, January 12, 2022 - 8:12:11 PM


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Rosy Tsopra, Xose Fernandez, Claudio Luchinat, Lilia Alberghina, Hans Lehrach, et al.. A framework for validating AI in precision medicine: considerations from the European ITFoC consortium. BMC Medical Informatics and Decision Making, BioMed Central, 2021, 21 (1), pp.274. ⟨10.1186/s12911-021-01634-3⟩. ⟨inserm-03373629⟩



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