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. Furthermore, this platform will exploit federated technologies for reproducing unidentifiable data from closed borders, promoting the indirect assessment of a broader number of databases, while respecting 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. The AISym4Med project 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.

ABOUT THE PROJECT

AIsym4MED aims to remove the barriers that prevent the deployment of high quality solutions. To achieve this, we will develop data generation, model auditing and visualisation tools that respect the fundamental rights and diversity of humanity, giving us a new perspective and vision of how we can help people achieve a better state of well-being.

system-augmented-with-controlled-data-synthesis-for-experimentation-and-modeling-purposes

CHALLENGES


Machine learning algorithms need an immense amount of data to have high performance in real-world scenarios.


Although there is a vast amount of stored data in a range of healthcare facilities, accessibility is hindered by several issues, such as privacy and anonymization concerns.


Healthcare data suffers from incompleteness and lack of quality and does not always follow a standard format.


Healthcare data is directly connected to the diversity of human beings’ biology; hence it is important to consider the representativeness of sociodemographic groups.


Different pathologies, especially the rare ones, are not sufficiently represented in most scenarios.


Data-sharing processes are not always as transparent as they should be, while medical data contains personal and sensitive information which must be handled with privacy and security.


It is difficult for innovators and researchers to comply with responsible AI principles when developing and deploying new solutions, which are critical for ethical standards.


There are relevant differences to implement fair and trustworthy AI systems which address real-life problems ethically, effectively, and efficiently

Expected IMPACTS

AISym4Med will primarily pursue Scientific impact pathways, contributing to the generation of relevant scientific impacts, by promoting scientific excellence in the creation and dissemination of new knowledge and by providing relevant and validated evidence on real-world applications of the developed solution. AISym4Med will also contribute to Economic/technological impacts, by validating the possibility for a trustworthy dataset system to be applied in complex scenarios and adapt to the current data availability in cross border situations. In addition, AISym4Med will contribute to societal impacts, by promoting health data sharing (regional, national and internationally) ensuring compliance with ethical and legal requirements (GDPR, cybersecurity, etc.), and thus fostering cooperation between the different stakeholders in the health domain, by generating new synthetic data avoiding any kind of biases and by improving human acceptance of AI.

digital-technologies

01

Health industry in the EU is more competitive and sustainable, assuring European leadership in breakthrough health technologies and strategic autonomy in essential medical supplies and digital technologies, contributing to job creation and economic growth, in particular with small- and medium-sized enterprises (SMEs).

02

Health industry is working more efficiently along the value chain from the identification of needs to the scaleup and take-up of solutions at national, regional, or local level, including through early engagement with patients, health care providers, health authorities and regulators ensuring suitability and acceptance of solutions

03

European standards, including for operations involving health data, ensure patient safety and quality of healthcare services as well as effectiveness and interoperability of health innovation and productivity of innovate

04

Citizens, health care providers and health systems benefit from a swift uptake of innovative health technologies and services offering significant improvements in health outcomes, while benefits from decreased time-to-market

Expected Outcomes

01

GDPR-compliant-guidelines

EU contributes strongly to global standards for health data through enhancement of common European standards for health data (including medical imaging data) by researchers and innovators. Researchers and innovators contribute to GDPR compliant guidelines and rules for data anonymization

02

data-driven-digital-solutions

Innovators have access to advanced secure data processing tools to test and develop robust data-driven digital solutions and services in response to the needs of researchers, clinicians, and health systems at large.

03

innovation-process-providing-secure

Cross-border health data hubs facilitate innovation process providing secure, trustable testing environments

04

Clinicians_-patients-and-individuals

Clinicians, patients and individuals use a larger variety of high-quality data tools and services for wellbeing, prevention, diagnosis, treatment, and follow-up of care.

05

GDPR-compliant-data-driven-solutions

Researchers and innovators have more opportunities for testing and developing GDPR compliant data driven solutions based on actual needs of the health care environments.

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