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Synthetic and scalable data platform for medical empowered AI

Project description

DEENESFRITPL

A platform for healthcare data engineers

The EU-funded AISym4Med project aims to create a platform to support the development of digital solutions and services for medical applications by enabling innovators to evaluate and improve AI systems in a secure and trustworthy environment. The platform will include two main modules - the Model Auditor, which will allow transparent evaluation of AI models in terms of performance, biases, limitations, and GDPR compliance, and the Data Synthesiser, which will provide new data instances to enhance more robust models for better representativeness of underrepresented groups and privacy preservation. The platform will also address data security, data quality control, anonymisation, attribute-based privacy measures and ethical norms while promoting the indirect assessment of a broader number of data sets through a federated approach.

Objective

AISym4Med aims at developing a platform that will provide healthcare data engineers, practitioners, and researchers access to a trustworthy dataset system augmented with controlled data synthesis for experimentation and modeling purposes. This platform will address data privacy and security by combining new anonymization techniques, attribute-based privacy measures, and trustworthy tracking systems. Moreover, data quality controlling measures, such as unbiased data and respect to ethical norms, context-aware search, and human-centered design for validation purposes will also be implemented to guarantee the representativeness of the synthetic data generated. Indeed, an augmentation module will be responsible for exploring and developing further the techniques of creating synthetic data, also dynamically on demand for specific use cases. Furthermore, this platform will exploit federated technologies for reproducing un-indentifiable data from closed borders, promoting the indirect assessment of a broader number of databases, while respecting the privacy, security, and GDPR-compliant guidelines. The proposed framework will support the development of innovative unbiased AI-based and distributed tools, technologies, and digital solutions for the benefit of researchers, patients, and providers of health services, while maintaining a high level of data privacy and ethical usage. AISym4Med will help in the creation of more robust machine learning (ML) algorithms for real-world readiness, while considering the most effective computation configuration. Furthermore, a machine-learning meta-engine will provide information on the quality of the generalized model by analyzing its limits and breaking points, contributing to the creation of a more robust system by supplying on-demand real and/or synthetic data. This platform will be validated against local, national, and cross-border use-cases for both data engineers, ML developers, and aid for clinicians’ operations.

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Call for proposal

HORIZON-HLTH-2022-IND-13

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Funding Scheme

HORIZON-RIA - HORIZON Research and Innovation Actions

Coordinator

ASSOCIACAO FRAUNHOFER PORTUGAL RESEARCH
Net EU contribution
€ 1 051 552,50
Address
RUA ALFREDO ALLEN 455/461
4200-135 Porto
Portugal

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Region
Continente Norte Área Metropolitana do Porto
Activity type
Research Organisations
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Total cost
€ 1 051 552,50

Participants (12)

Partners (2)

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Last update: 24 May 2023

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Permalink: https://cordis.europa.eu/project/id/101095387

European Union, 2024