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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
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
Source :
BMC Medical Informatics & Decision Making. 10/4/2021, Vol. 21 Issue 1, p1-14. 14p.
Publication Year :
2021

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
14726947
Volume :
21
Issue :
1
Database :
Academic Search Index
Journal :
BMC Medical Informatics & Decision Making
Publication Type :
Academic Journal
Accession number :
152790614
Full Text :
https://doi.org/10.1186/s12911-021-01634-3