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Grant Analysis

Purpose & Target

Single, clear statement of grant's core objective: This grant aims to advance biomedical research by leveraging multimodal data to develop and apply robust, trustworthy, and ethical Generative Artificial Intelligence (AI) models, ultimately contributing to predictive and personalised medicine.
Explicit identification of target recipient type and size: The grant targets consortia comprising a broad representation of stakeholders, including industry (SMEs, leading-edge startups), academia (universities), and healthcare professionals. The Joint Research Centre (JRC) may also participate.
MUST state if grant is 'SECTOR-SPECIFIC' or 'SECTOR-AGNOSTIC': This is a SECTOR-SPECIFIC grant.
Geographic scope and any location requirements: Funding is available for legal entities established in EU Member States, Horizon Europe Associated Countries (as defined in Annex B of the Work Programme General Annexes), and specifically, legal entities established in the United States of America.
Key filtering criteria for initial grant screening: Proposals must focus on Generative AI, biomedical research, and the use of large-scale multimodal health data, with a clear pathway to predictive and personalised medicine. Expertise in social sciences and humanities (SSH) is also required.
Grant frequency and program context: This is a single-stage call for 2025 within the 'Cluster 1 - Health' of the Horizon Europe framework programme, specifically addressing the destination 'Developing and using new tools, technologies and digital solutions for a healthy society'. It is driven by the EU's Biotechnology and Biomanufacturing Strategy and Artificial Intelligence Strategy.

Financial Structure

Total budget for the HORIZON-HLTH-2025-01-TOOL-03 topic for the year 2025 is EUR 50,000,000.
The expected number of grants for this topic is 3.
The minimum expected contribution per grant is EUR 15,000,000.
The maximum expected contribution per grant is EUR 17,000,000.
Currency for funding is EUR.
Proposals are expected to include a budget for the attendance to regular joint meetings to facilitate collaboration among funded projects.
Costs of any other potential joint activities may also be considered in the budget.

Eligibility Requirements

Organizational Type & Legal Status

Eligible organization types include: industry (e.g., EU industrial developers of Generative AI solutions, leading-edge startups, SMEs), academia (e.g., universities), and healthcare professionals.
Any legal entity established in the United States of America is eligible to receive Union funding.
The Joint Research Centre (JRC) may participate as a member of the consortium selected for funding.

Consortium Requirements

A consortium is required, demonstrating a broad representation of stakeholders (e.g., industry, academia, healthcare professionals).

Geographic Location

Eligible countries are those described in Annex B of the Horizon Europe Work Programme General Annexes (generally EU Member States and Horizon Europe Associated Countries).
Legal entities established in the United States of America are specifically eligible to receive Union funding.

Technical & Operational Capacity

Applicants must demonstrate financial and operational capacity as described in Annex C of the Work Programme General Annexes.
If projects use satellite-based earth observation, positioning, navigation, and/or related timing data and services, beneficiaries must use Copernicus and/or Galileo/EGNOS (other data and services may additionally be used).
Projects must ensure the effective contribution of social sciences and humanities (SSH) disciplines and involve SSH experts and institutions.

Exclusion Criteria

Entities assessed as “high-risk suppliers” of mobile network communication equipment (and any entities they own or control) are not eligible to participate as beneficiaries, affiliated entities, or associated partners if the topic is identified as “subject to restrictions for the protection of European communication networks”.
The creation and expansion of health data and/or AI infrastructures or large-data curation initiatives, whether existing or under development, are not within the scope of this topic.

Application Process

Submission Process

The submission session became available on 2025-05-22.
The application deadline for this call is 2025-09-16 00:00:00+0000.
The submission procedure is single-stage.
Proposals must be submitted via the Funding & Tenders Portal using the standard application form (HE RIA, IA) available in the Submission System.

Required Documentation & Materials

Proposals must adhere to the specified page limits and layout described in Part B of the Application Form.
Applicants intending to include clinical studies must provide details in a dedicated annex using the template provided in the submission system.

Evaluation Process

Evaluation and award criteria, scoring, and thresholds are detailed in Annex D of the Work Programme General Annexes (external document).
The submission and evaluation processes are described in Annex F of the Work Programme General Annexes and the Online Manual.
An indicative timeline for evaluation and grant agreement is provided in Annex F of the Work Programme General Annexes.
A standard evaluation form (HE RIA, IA) will be used, adapted as necessary.

Post-Award Requirements

The legal and financial set-up of the grants is described in Annex G of the Work Programme General Annexes (external document).
Successful proposals are strongly encouraged to collaborate, for example, by participating in networking and joint activities, exchanging knowledge, developing/adopting best practices, or engaging in joint communication activities.
The details of these joint activities will be defined during the grant agreement preparation phase.
Beneficiaries are encouraged to utilize resources offered by AI factories, when relevant and in accordance with specific access terms and conditions.

Evaluation Criteria

Scoring and Thresholds

Proposals will be evaluated based on three criteria: Excellence, Impact, and Quality and Efficiency of the Implementation.
The threshold for each criterion is a minimum score of 4.
The cumulative threshold for all criteria combined is 12.

Excellence

Development of new or re-purposing of existing Generative AI models for biomedical research across various medical fields and/or therapeutic indications.
Models must be robust and based on the use of large-scale, complex, and multimodal high-quality data (real and/or synthetic), including but not limited to medical imaging, genomics, proteomics, other molecular data, electronic health records, laboratory results, unstructured health data, and/or available scientific and public information.
Justification of the relevance of chosen biomedical data and/or combinations for training and optimization of Generative AI tools.
Integration of multidisciplinary approaches and a broad representation of stakeholders (e.g., industry, academia, healthcare professionals) within the consortia.

Impact

Development of a proof of concept with at least two use cases relevant for predictive and personalised medicine in different medical fields.
Demonstration of scientific added value compared to currently used methods and/or potential future clinical utility of the Generative AI models in biomedical research.
Active engagement of potential end users in the development, adaptation, and testing of new/repurposed models, considering sustainability aspects.
Development or revision of existing methodologies to assess alignment with human values and the use cases of developed/repurposed Generative AI models, their applicability, performance, limitations, and added value.
Methodologies must demonstrate the technical, scientific, and potential future clinical utility, robustness, and trustworthiness of the models.
Inclusion of appropriate performance metrics for continuous evaluation and testing of scientific/technical robustness and relevance, including risks from misalignment of training data (e.g., hallucinations, confabulations).
Inclusion of appropriate metrics for model intelligibility, robustness, alignment with ethical principles, and approaches for ethical evaluation of AI trustworthiness.
Appropriate solutions to identify and mitigate potential bias and confounding (e.g., representativeness of data, bias of trainer, bias of training/validation data, algorithmic discrimination, gender bias).
Methods to systematically address and assess Ethical, Legal, and Societal Implications (ELSI) aspects, including data privacy, risk of discrimination/bias (not limited to sex, gender, age, disability, race/ethnicity, religion, belief, minority/vulnerable groups).
Appropriate techniques to ensure explainability of the model to increase user trust.
Approaches and metrics (where feasible) for the usability of Generative AI models for researchers.
Demonstration of EU added value by developing and/or using trustworthy and ethical Generative AI models developed in the EU and Associated countries.
Involvement of EU industrial developers of Generative AI solutions, including leading-edge startups where possible.

Implementation

Quality and efficiency of the work plan and proposed management structure.
Capacity and expertise of the consortium members in relation to the project objectives.
Consideration for a budget covering attendance at regular joint meetings for collaboration purposes with other funded projects.
Encouragement of an open-source approach when technically and economically feasible.
Exploitation of synergies with other relevant projects funded under Horizon Europe and/or Digital Europe Programmes.
Leverage of knowledge/data platforms already developed by specific Horizon Europe Partnerships or missions (e.g., European Partnership on Rare Diseases, Cancer Mission).
Encouragement to leverage available and emerging European data infrastructures (e.g., European Health Data Space, European Genomic Data Infrastructure, Cancer Image Europe, European Open Science Cloud, EBRAINS) whenever relevant.
Adoption of EOSC recommendations and services for high-quality software (if applicable).
Proposals should consider the involvement of the European Commission's Joint Research Centre (JRC) for interface between research activities and pre-normative regulatory science.

Compliance & Special Requirements

Regulatory & Ethical Compliance

Adherence to FAIR data principles for all data used in the project.
Application of GDPR compliant processes for personal data protection, based on established good practices.
Projects must promote the highest standards of transparency and openness of models, extending beyond documentation to include assumptions, code, and FAIR data management.
Systematic assessment and addressing of Ethical, Legal, and Societal Implications (ELSI) aspects, including data privacy and the risk of discrimination/bias (e.g., based on sex, gender, age, disability, race/ethnicity, religion, belief, minority, or vulnerable groups).
Compliance with the Ethics Guidelines for Trustworthy AI.
Adherence to the Guidelines on the responsible use of Generative AI in research developed by the European Research Area Forum (e.g., refraining from using GenAI tools in sensitive activities like peer reviews, respecting privacy, confidentiality, and intellectual property rights).

Technical & Data Specifications

Projects must use large-scale, complex, and multimodal high-quality data (real and/or synthetic) to train and optimize Generative AI models.
An open-source approach is encouraged for developed solutions when technically and economically feasible.
Applicants are encouraged to leverage available and emerging European data infrastructures (e.g., European Health Data Space, European Genomic Data Infrastructure, Cancer Image Europe, European Open Science Cloud, EBRAINS) whenever relevant.

Security & Risk Management

Protection of European communication networks is a security interest; high-risk suppliers of mobile network communication equipment are not eligible.
Methodologies for assessing GenAI models must include appropriate solutions to identify and mitigate potential bias and confounding.

Cross-Cutting & Special Considerations

Projects require the effective contribution of social sciences and humanities (SSH) disciplines and the involvement of SSH experts and institutions.
Synergies with other relevant projects funded under Horizon Europe and/or Digital Europe Programmes are encouraged.
Leveraging knowledge/data platforms from specific Horizon Europe Partnerships or missions (e.g., European Partnership on Rare Diseases, Cancer Mission) is encouraged.
The definition of clinical studies is broad; applicants should review it thoroughly before submitting their application.
Strategic alignment with the EU's Biotechnology and Biomanufacturing Strategy and Artificial Intelligence Strategy is key.
Proposals align with the Strategic Technologies for Europe Platform (STEP) objectives to boost investments in critical technologies in Europe.

Grant Details