Please use this identifier to cite or link to this item:
10.1186/s12911-021-01634-3
Title: | A framework for validating AI in precision medicine : considerations from the European ITFoC consortium |
Authors: | Tsopra, Rosy Fernandez, Xose Luchinat, Claudio Alberghina, Lilia Lehrach, Hans Vanoni, Marco Dreher, Felix Sezerman, O Ugur Sezerman Cuggia, Marc de Tayrac, Marie Miklaševičs, Edvīns Itu, Lucian Mihai Geanta , Marius Ogilvie, Lesley Godey, Florence Boldisor, Cristian Nicolae Campillo-Gimenez , Boris Cioroboiu, Cosmina Ciusdel, Costin Florian Coman , Simona Cubelos, Oliver Hijano 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 RSU Institute of Oncology |
Keywords: | Artificial intelligence;Precision medicine;Personalized medicine;Computerized decision support systems;cancer;oncology;1.2 Computer and information sciences;3.1 Basic medicine;1.1. Scientific article indexed in Web of Science and/or Scopus database;SDG 3 - Good Health and Well-being |
Issue Date: | 2-Oct-2021 |
Citation: | Tsopra , R , Fernandez , X , Luchinat , C , Alberghina , L , Lehrach , H , Vanoni , M , Dreher , F , Sezerman , O U S , Cuggia , M , de Tayrac , M , Miklaševičs , E , Itu , L M , Geanta , M , Ogilvie , L , Godey , F , Boldisor , C N , Campillo-Gimenez , B , Cioroboiu , C , Ciusdel , C F , Coman , S , Cubelos , O H , Itu , A , Lange , B , Le Gallo , M , Lespagnol , A , Mauri , G , Soykam , H O , Rance , B , Turano , P , Tenori , L , Vignoli , A , Wierling , C , Benhabiles , N & Burgun , A 2021 , ' A framework for validating AI in precision medicine : considerations from the European ITFoC consortium ' , BMC Medical Informatics and Decision Making , vol. 21 , no. 1 , 274 . https://doi.org/10.1186/s12911-021-01634-3 |
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. |
Description: | Funding Information: This work was supported by the ITFoC project (Information Technology for the Future of Cancer) – FLAG-ERA support. Publisher Copyright: © 2021, The Author(s). |
DOI: | 10.1186/s12911-021-01634-3 |
ISSN: | 1472-6947 |
Appears in Collections: | Research outputs from Pure / Zinātniskās darbības rezultāti no ZDIS Pure |
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A_framework_for_validating_AI_in_precision.pdf | 1.62 MB | Adobe PDF | View/Open |
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