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Deployment of cutting-edge multi-modal AI-based solutions in medical imaging

European Commission

Expected Outcome:

New data services and user tools for the Cancer Image Europe platform covering data curation / inclusion by data providers, data quality assurance and compliance checks, and risk/security management in alignment with agreed standards, procedures and requirements, including the framework of the European Health Data Space.
Multi-centre validation of AI-driven solutions in the field of medical imaging, including also evidence generation for clinical utility and cost efficiency.
Deployment of multi-modal, cutting-edge AI-driven solutions in the field of medical imaging in healthcare and research settings, leveraging different types of data (at least one imaging modality and one other data type e.g. genomics, other omics, laboratory results, real-world data etc.) and building on the achievements of the Cancer Image Europe platform.
Training and upskilling of medical imaging personnel and/or healthcare professionals for the deployed technology and further use.

Objective:

Accelerate the uptake of EU AI-driven solutions that are ready to be deployed in healthcare settings for patient care and for research purposes. This will facilitate the paradigm shift in the digital transformation of healthcare towards personalised medical solutions;
Facilitate the deployment of EU cutting-edge AI-driven solutions in medical imaging, combined with other health data, for increased efficiency and better patient outcomes, leveraging the Cancer Image Europe platform;
Further develop the data, testing and validation services and user tools of Cancer Image Europe platform in alignment with the legal and technical framework of the European Health Data Space, also towards supporting the development and uptake of EU cutting-edge multi-modal AI-based solutions in medical imaging (including Deep learning and Generative AI solutions) for healthcare;
Ensure alignment and inter-operability of the Cancer Image Europe platform with the HealthData@EU infrastructure of the EHDS;
Conduct multi-centre validation studies of promising medical imaging-based and multi-modal AI solutions for screening, early detection, diagnosis and care, generating evidence for clinical utility and cost effectiveness of tested solutions.

Scope:

By building on, extending and leveraging the Cancer Image Europe platform, this action is expected to facilitate the uptake of EU AI-driven solutions in medical imaging (including Machine Learning and Generative AI), towards their deployment in clinical settings. It should also include upskilling of healthcare professionals and evidence generation to evaluate the performance of the deployed AI-driven solutions and engage patients.

The action should also contribute to upgrading and enhancing the uptake of the Cancer Image Europe platform through deployment of tools, application programming interfaces (APIs) and interfaces enabling high-quality data services for the users of the Cancer Image Europe platform. The solutions should cover, at least, data quality assessment, including security, data access and analysis, as well as services for AI developers supporting their regulatory compliance pathway, including the EHDS Regulation, if applicable. Moreover, data services will cover comprehensive data quality assessment—including security, data access, and analysis. Specifically, tools will be deployed to ensure that datasets federated in Cancer Image Europe are fully compliant with EHDS, utilizing Health DCAT-AP for metadata descriptions and adhering to the data quality and utility label standards set by the EHDS Regulation.

Moreover, the action should consider synergies and cross-fertilisation of tools with other relevant health data infrastructures: HealthData@EU, 1+Million Genomes (1+MG), Intensive Care Unit data infrastructure, the European Virtual Human Twins Initiative, and UNCAN.eu platform to further facilitate the integration and processing of different types of health data. Also, the outcomes of EU-funded projects dealing with other imaging modalities, such as digital pathology (e.g. BigPicture project), should be considered. Finally, synergies with the ongoing AI related activities of the Health Data Access Bodies (HDABs) in the EHDS, the SHAIPED project and the TEF-Health project should be leveraged.

AI Based Application Success Predictor

1️⃣ Strong, Mission-Aligned Impact (Most Important Across EC Calls)

The EC is impact-driven: proposals must show how the project will:

Solve a major European or global societal challenge

Deliver measurable, lasting benefits for EU citizens

Produce outputs that can be used by policymakers, industry, or society

Align with Horizon Europe missions, priorities, and strategic agendas

Predictor: Clear, quantifiable, EU-level impact → strongest scoring factor.

2️⃣ Clear, Ambitious, but Achievable Objectives

Successful proposals show:

2–4 well-defined objectives linked to the Work Programme call text

Clearly articulated research questions or innovation goals

Logical, realistic expected outcomes and deliverables

Feasible scientific and technical approaches

Predictor: Balanced ambition + feasibility.

3️⃣ Excellent, Cutting-Edge Science or Innovation

For RIA/IA/CSA or ERC-level grants, reviewers expect:

High novelty and innovation

Strong grounding in current state-of-the-art

Clear advancement beyond existing approaches

Solid theoretical or experimental foundations

Robust methodological design

Predictor: Scientific excellence is essential for competitive scoring.

4️⃣ Strong Consortium with Complementary Expertise

EC proposals are consortium-driven (except ERC/EIC Accelerator).

High-scoring consortia:

Cover all needed competencies (science, industry, policy, ethics, dissemination)

Include SMEs, industry partners, NGOs, and public bodies when relevant

Are geographically diverse across EU Member States and Associated Countries

Demonstrate strong leadership and communication structures

Predictor: Well-constructed consortium with clear roles.

5️⃣ Clear Pathway From Outputs → Outcomes → Impact

Evaluators look for a credible trajectory showing:

How research leads to specific outputs (data, tools, prototypes)

How outputs lead to uptake or use

How use produces societal, economic, scientific, or policy impact

Strong Key Performance Indicators (KPIs) and impact metrics

Predictor: Clearly mapped impact pathway.

6️⃣ Strong Implementation Plan (Work Packages, Deliverables, Gantt Chart)

Winning proposals have:

Well-designed Work Packages (WPs) with clear scope and responsibilities

Interdependencies identified and risk-mitigation strategies

Detailed milestones and deliverables

Feasible budget aligned with tasks

Strong project management plan

Predictor: High implementation quality boosts the “Excellence” and “Implementation” scores.

7️⃣ Policy Relevance and Contribution to EU Strategies

Especially critical for health, climate, digital, and social calls.

Proposals score higher when they link to:

EU Cancer Mission

EU Green Deal

Digital Europe strategy

EU Biodiversity Strategy

EU Health Union & One Health

Open Science & FAIR data mandates

Predictor: Clear alignment with EU policies.

8️⃣ Strong Stakeholder & Citizen Engagement (Especially in Social & Health Missions)

EC values inclusivity:

Patient groups

Civil society organizations

Public sector bodies

Regulatory agencies

Citizen science components

Stakeholder letters of intent or commitment strengthen credibility.

Predictor: Engagement adds impact and relevance.

9️⃣ Robust Data Management, Open Science, and Ethics

Mandatory components include:

FAIR Data Management Plan

Open access publications

Ethics self-assessment

GDPR compliance

Data security, governance, and ethical approvals

Animal-use reduction and justification (if applicable)

Predictor: Clear compliance with ethical and data obligations.

10️⃣ Well-Justified Budget and Resource Allocation

Budget must be:

Proportional to tasks

Transparent and reasonable

Efficiently distributed among partners

Free from padding or unjustified costs

Predictor: Realistic budgets improve Implementation scores.

🚫 COMMON PITFALLS THAT LEAD TO EC GRANT REJECTION

Pitfall	Why It Fails
Weak connection to Work Programme text	Immediate score reduction
Vague or generic impact statements	Poor Impact score
Overly ambitious, unrealistic scope	Feasibility concerns
Poorly structured consortium	Low Implementation score
No policy relevance	Weak strategic alignment
Lack of concrete KPIs or outcomes	Impact unclear
Weak data or ethics plan	Eligibility/score penalties
No exploitation or dissemination plan	Insufficient impact credibility
Budget misalignment	Reviewer distrust

Specialties

Clinical Trials

Eligibility Requirements

In order to be eligible, the applicants (beneficiaries and affiliated entities) must:

− be legal entities (public or private bodies)

− be established in one of the eligible countries, i.e.:

− EU Member States (including overseas countries and territories (OCTs))

− non-EU countries (except for topics with restrictions; see below):

− listed EEA countries and countries associated to the Digital Europe Programme (list of participating countries) Beneficiaries and affiliated entities must register in the Participant Register

— before submitting the proposal

— and will have to be validated by the Central Validation Service (REA Validation). For the validation, they will be requested to upload documents showing legal status and origin. Other entities may participate in other consortium roles, such as associated partners, subcontractors, third parties giving in-kind contributions, etc (see section 13).

Sponsor Details

Sponsor Institute/Organizations: European Commission

Sponsor Type: Corporate/Non-Profit

Address: Rue de la Loi 200 / Wetstraat 200, 1049 Brussels

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