Data Management Plan

 

Data Management Plans (DMPs) are a key element of good data management. A DMP describes the data management life cycle for the data to be collected, processed and/or generated within a project.

As part of making research data findable, accessible, interoperable and re-usable (FAIR). A DMP typically includes information on:

- the handling of research data during & after the end of the project

- what data will be collected, processed and/or generated

- which methodology & standards will be applied

- whether data will be shared/made open access and

- how data will be curated & preserved; including after the end of the project.

Virtual testing services catalog vs1

 

We will set up virtual testing and experimentation facilities and services AI in healthcare. Subobjectives are:

-set up the evaluation protocols, tools and digital facilities to provide virtual testing services based on datasets

-set up the evaluation protocols, tools and digital facilities to provide virtual testing services based on simulator and mixed testing environments (coupling simulators and physical test benches).

-set up the evaluation protocols, tools and digital facilities to provide cybersecurity testing services.

-set up a cross European digital testing platform and the TEF dataspace that will allow access to certain services and databases remotely.

Catalogue of testing centers

 

A catalogue on physical and virtual testing centers will be available to interested endusers.

The real testing facilities (hospital platforms, physical infrastructures, living labs, etc.) and the laboratory testing ones.
We will carry out tests and experiments of AI solutions (software and hardware) in large-scale and sustainable real or realistic environments.

Provide access to clinical settings is crucial, and not only to conduct clinical trials and use clinical datasets of infrastructures, but also to get feedbacks from healthcare professionals and patients.

In addition, we will set up virtual testing and experimentation facilities and services AI in healthcare.

Catalogue of quality criteria and reference metrics

 

We will define quality criteria and reference metrics

- to evaluate data for AI

- to evaluate AI algorithms

-to establish standards for agile certification process.

Price lists

 

To comply with state aid under GBER, the TEF draws up a price list (differentiated per node) for the services offered, based on market prices
if these exist, and display the available reductions it plans to offer to SMEs.

Larger companies must pay the market prices according to the TEF’s price list.

While an initial catalogue of services with price lists and reductions for SMEs has been developed,

TEF-Health commits to further advance its methodology on deriving its price lists.

Roadmap of the virtual testing facilities

 

We will set up a setting up virtual testing and experimentation facilities and services AI in healthcare.

These facilities and services will be based on datasets and/or simulators, cybersecurity. Furthermore we aim at setting up a digital testing platform to make several virtual testing services remotely accessible, when conditions allow (non-confidential data, confidential algorithms, etc.).

Full compliance with the ethical requirements will be ensured.

 

Exemplary agile certification compliant with MDR

 

We will develop reference process for ‘agile certification’ of medical devices according to MDR.

TEF-Health will

-establish links to notified bodies regarding procedure, touchpoints, and possible legal requirements/changes for an agile certification approach.

-use a small number of use cases based on existing / soon to be launched products, conduct exemplary certifications to prove out / improve the agile certification.

Demonstrator cases

 

By interactively evaluating potential use cases, we will promote TEF-SME interaction and guide the TEF in selecting representative and strategically suitable internally and externally requested demonstrator cases.

A demonstrator case might e.g.

- use an already certified AI product, execute agile certification,

- evaluate benefits of agile over standard certification,  

- use results to fine-tuneagile certification,

- apply refined process to certify a new AI product.

 

Standards for AI in medical devices

 

TEF-Health aims to establish an agile certification process for AI in medical devices which aims to reduce time-to-market for these products, promote innovation in AI in health care by SMEs while maintaining high quality standards that make AI in medical devices trustworthy.