Statistical and AI based Methods for Advanced Clinical Trials CHallenges in Digital Health


Coordinators : Sarah  Zohar and Rodolphe Thiébaut

Coordinating institution : Inria

Key words

Experimental designs, interventional studies, clinical trials, statistics, artificial intelligence, machine learning, adaptive designs, digital medical devices, multi-modal data, multi-source data, highdimensional health data, hybrid designs, computational models


Health-related interventions and their evaluation have been revolutionized by advances in biotechnology and digitization and expectations are high. To support the acceleration of drug development and other medical interventions through the use of artificial intelligence (AI) and data from a variety of sources, there is a need for a clear understanding of the appropriateness of this use and for robust trial evaluation methods. The need to adapt the methods used and to propose robust innovative approaches to clinical trials is increasingly critical, and for example, the proliferation of new digital medical devices using AI alone requires specific methods to evaluate their ultimate impact on health.

The objective of the SMATCH project is therefore to develop and apply statistical and AI-based methods with the ultimate goal of accelerating the development of medical interventions (drugs and digital medical devices) during their evaluation in clinical trials based on the following assumptions:

  • The use of information generated in preclinical studies (animal studies, organoids, in silico studies) combined with adaptive designs should help the early phases of development ;
  • The integration of multi-source data including real-world and in silico data should help to complete trials;
  • Specific adaptive designs should be defined for the evaluation of digital medical devices based on learning algorithms.

To this end, SMATCH is structuring its research around four operational workpackages:

  • Research of new clinical trial methods and designs based on the translation of research-based disease models from animals to humans;
  • Development of new approaches for enriching clinical trials with multi-source and multi-dimensional ancillary data;
  • Development of next generation designs for clinical evaluation of digital medical devices based on artificial intelligence algorithms;
  • Evaluation with regulatory authorities and end-users of the regulatory impact and feasibility of innovative methods for clinical trials proposed for widespread use.

The consortium is made up of 16 teams, mainly from Inria and Inserm Centers recognized in this field, bringing a unique and complementary expertise in data sciences and AI applied to health problems and specifically to clinical trials.

In addition, links with the regulatory bodies involved are already established within the consortium (e.g. HAS) and outside (e.g. EMA). Moreover, all methodological projects are applied to ongoing health studies in various fields.

Finally, many connections exist with the other axes of the PEPR Digital Health and more generally with the projects carried out within the framework of the digital health acceleration strategy.

Thus, by providing innovative and adapted methodological tools that will have already been applied in a real context, we hope to participate in the acceleration of clinical research leading to major societal and economic impacts.

Laboratory or department, team Supervisors
CRC – U 1138, Eq HEKA

BPH – U1219, Eq SISTM

Inserm, Inria, Sorbonne University, Paris Cité University

Inserm, Inria, Bordeaux University

ISPED, CHU Bordeaux partners

Idesp – UA11    Eq PreMeDICaL Inserm, Inria, Montpellier University

CHU Montpellier partner

Centre Inria Saclay – Eq SODA Inria, Paris-Saclay University
IAME – U 1137, Eq BIPID


Inserm,  Paris Cité University, Paris 13 University

University of Rennes, Inserm, EHESP, IRSET

CRESS-U 1153, Eq METHODS Inserm, Paris Cité University, Paris 13 University, INRAE

CNAM partner

CRESS-U 1153, Eq ECSTRA Inserm, Paris Cité University, Paris 13 University, INRAE

CNAM partner

SPHERE – U 1246 Inserm, Tours University, Nantes University

CHU Tours partner

CIC 1402 Inserm, Poitiers University

Eq SCOOl, Centre Inria Lille

CNRS, Inria, Lille University

Centrale et IMT partners

EUCLID – CIC EC 1401 Inserm, CHU Bordeaux
BIOMAPS – U 1281, UMR 9011 Inserm, CEA, CNRS, Paris-Saclay University

SHFJ partner

Pole Recherche Clinique – iT7 Inserm, Institut Santé Publique
Mission numérique en santé HAS – Haute Autorité de Santé