BMC Medical Informatics and Decision Making (Oct 2021)

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

  • Rosy Tsopra,
  • Xose Fernandez,
  • Claudio Luchinat,
  • Lilia Alberghina,
  • Hans Lehrach,
  • Marco Vanoni,
  • Felix Dreher,
  • O.Ugur Sezerman,
  • Marc Cuggia,
  • Marie de Tayrac,
  • Edvins Miklasevics,
  • Lucian Mihai Itu,
  • Marius Geanta,
  • Lesley Ogilvie,
  • Florence Godey,
  • Cristian Nicolae Boldisor,
  • Boris Campillo-Gimenez,
  • Cosmina Cioroboiu,
  • Costin Florian Ciusdel,
  • Simona Coman,
  • Oliver Hijano Cubelos,
  • Alina Itu,
  • Bodo Lange,
  • Matthieu Le Gallo,
  • Alexandra Lespagnol,
  • Giancarlo Mauri,
  • H.Okan Soykam,
  • Bastien Rance,
  • Paola Turano,
  • Leonardo Tenori,
  • Alessia Vignoli,
  • Christoph Wierling,
  • Nora Benhabiles,
  • Anita Burgun

DOI
https://doi.org/10.1186/s12911-021-01634-3
Journal volume & issue
Vol. 21, no. 1
pp. 1 – 14

Abstract

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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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