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

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 limit...

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Autores principales: Tsopra, Rosy, Fernandez, Xose, Luchinat, Claudio, Alberghina, Lilia, Lehrach, Hans, Vanoni, Marco, Dreher, Felix, Sezerman, O.Ugur, Cuggia, Marc, de Tayrac, Marie, Miklasevics, Edvins, Itu, Lucian Mihai, Geanta, Marius, Ogilvie, Lesley, Godey, Florence, Boldisor, Cristian Nicolae, Campillo-Gimenez, Boris, Cioroboiu, Cosmina, Ciusdel, Costin Florian, Coman, Simona, Hijano Cubelos, Oliver, Itu, Alina, Lange, Bodo, Le Gallo, Matthieu, Lespagnol, Alexandra, Mauri, Giancarlo, Soykam, H.Okan, Rance, Bastien, Turano, Paola, Tenori, Leonardo, Vignoli, Alessia, Wierling, Christoph, Benhabiles, Nora, Burgun, Anita
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8487519/
https://www.ncbi.nlm.nih.gov/pubmed/34600518
http://dx.doi.org/10.1186/s12911-021-01634-3
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author Tsopra, Rosy
Fernandez, Xose
Luchinat, Claudio
Alberghina, Lilia
Lehrach, Hans
Vanoni, Marco
Dreher, Felix
Sezerman, O.Ugur
Cuggia, Marc
de Tayrac, Marie
Miklasevics, Edvins
Itu, Lucian Mihai
Geanta, Marius
Ogilvie, Lesley
Godey, Florence
Boldisor, Cristian Nicolae
Campillo-Gimenez, Boris
Cioroboiu, Cosmina
Ciusdel, Costin Florian
Coman, Simona
Hijano Cubelos, Oliver
Itu, Alina
Lange, Bodo
Le Gallo, Matthieu
Lespagnol, Alexandra
Mauri, Giancarlo
Soykam, H.Okan
Rance, Bastien
Turano, Paola
Tenori, Leonardo
Vignoli, Alessia
Wierling, Christoph
Benhabiles, Nora
Burgun, Anita
author_facet Tsopra, Rosy
Fernandez, Xose
Luchinat, Claudio
Alberghina, Lilia
Lehrach, Hans
Vanoni, Marco
Dreher, Felix
Sezerman, O.Ugur
Cuggia, Marc
de Tayrac, Marie
Miklasevics, Edvins
Itu, Lucian Mihai
Geanta, Marius
Ogilvie, Lesley
Godey, Florence
Boldisor, Cristian Nicolae
Campillo-Gimenez, Boris
Cioroboiu, Cosmina
Ciusdel, Costin Florian
Coman, Simona
Hijano Cubelos, Oliver
Itu, Alina
Lange, Bodo
Le Gallo, Matthieu
Lespagnol, Alexandra
Mauri, Giancarlo
Soykam, H.Okan
Rance, Bastien
Turano, Paola
Tenori, Leonardo
Vignoli, Alessia
Wierling, Christoph
Benhabiles, Nora
Burgun, Anita
author_sort Tsopra, Rosy
collection PubMed
description 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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spelling pubmed-84875192021-10-04 A framework for validating AI in precision medicine: considerations from the European ITFoC consortium Tsopra, Rosy Fernandez, Xose Luchinat, Claudio Alberghina, Lilia Lehrach, Hans Vanoni, Marco Dreher, Felix Sezerman, O.Ugur Cuggia, Marc de Tayrac, Marie Miklasevics, Edvins Itu, Lucian Mihai Geanta, Marius Ogilvie, Lesley Godey, Florence Boldisor, Cristian Nicolae Campillo-Gimenez, Boris Cioroboiu, Cosmina Ciusdel, Costin Florian Coman, Simona Hijano Cubelos, Oliver Itu, Alina Lange, Bodo Le Gallo, Matthieu Lespagnol, Alexandra Mauri, Giancarlo Soykam, H.Okan Rance, Bastien Turano, Paola Tenori, Leonardo Vignoli, Alessia Wierling, Christoph Benhabiles, Nora Burgun, Anita BMC Med Inform Decis Mak Research Article 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. BioMed Central 2021-10-02 /pmc/articles/PMC8487519/ /pubmed/34600518 http://dx.doi.org/10.1186/s12911-021-01634-3 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Tsopra, Rosy
Fernandez, Xose
Luchinat, Claudio
Alberghina, Lilia
Lehrach, Hans
Vanoni, Marco
Dreher, Felix
Sezerman, O.Ugur
Cuggia, Marc
de Tayrac, Marie
Miklasevics, Edvins
Itu, Lucian Mihai
Geanta, Marius
Ogilvie, Lesley
Godey, Florence
Boldisor, Cristian Nicolae
Campillo-Gimenez, Boris
Cioroboiu, Cosmina
Ciusdel, Costin Florian
Coman, Simona
Hijano Cubelos, Oliver
Itu, Alina
Lange, Bodo
Le Gallo, Matthieu
Lespagnol, Alexandra
Mauri, Giancarlo
Soykam, H.Okan
Rance, Bastien
Turano, Paola
Tenori, Leonardo
Vignoli, Alessia
Wierling, Christoph
Benhabiles, Nora
Burgun, Anita
A framework for validating AI in precision medicine: considerations from the European ITFoC consortium
title A framework for validating AI in precision medicine: considerations from the European ITFoC consortium
title_full A framework for validating AI in precision medicine: considerations from the European ITFoC consortium
title_fullStr A framework for validating AI in precision medicine: considerations from the European ITFoC consortium
title_full_unstemmed A framework for validating AI in precision medicine: considerations from the European ITFoC consortium
title_short A framework for validating AI in precision medicine: considerations from the European ITFoC consortium
title_sort framework for validating ai in precision medicine: considerations from the european itfoc consortium
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8487519/
https://www.ncbi.nlm.nih.gov/pubmed/34600518
http://dx.doi.org/10.1186/s12911-021-01634-3
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