Expected Outcome:

This topic aims at supporting activities that are enabling or contributing to one or several expected impacts of destination “Developing and using new tools, technologies and digital solutions for a healthy society”. To that end, proposals under this topic should aim to deliver results directed towards and contributing to all the following expected outcomes:

• Researchers, including clinical researchers, have access to robust, trustworthy and ethical Generative Artificial Intelligence (AI)[1] models able to effectively advance biomedical research towards predictive and personalised medicine.
• Researchers, including clinical researchers, know how to use Generative AI models to synthesise the available scientific information and large-scale multimodal data and how to apply the necessary precautions, in order to deliver new knowledge and breakthrough scientific discoveries.
• Research community benefits from advanced methodologies to assess the validity and application of accurate, transparent, traceable, and explainable Generative AI models.

Scope:

The availability of large-scale multimodal health data, scientific information, and novel Generative AI models, combined with high-performance computing capacities offer an unprecedented opportunity for researchers to achieve breakthroughs in our understanding of disease development and to develop new predictive models for disease management, personalised treatment solutions and personalised care pathways. The European Commission recognises this potential and considers health research and healthcare, among the priority sectors for building the Union’s strategic leadership [COM(2024) 28 final].

This topic will contribute to advancing research and providing new evidence on how these models contribute to and support biomedical research and its applicability towards more predictive and personalised medicine, while also defining use conditions, usability requirements and training needs of the researchers. It aims to cover existing gaps related to Generative AI in biomedical research, addressing both capabilities and existing limitations.

Research actions under this topic should include all the following activities, ensuring multidisciplinary approaches and a broad representation of stakeholders in the consortia (e.g. industry, academia, healthcare professionals):

• Develop new or re-purpose existing Generative AI models for biomedical research across various medical fields and/or therapeutic indications. The models should be robust, based on the use of large-scale, complex, and multimodal high-quality data (real and/or synthetic data), such as 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 relevant to biomedical research. The applicants may choose any type of available large-scale biomedical data and/or their combinations and justify their relevance for training and optimisation of the Generative AI tools.
• Develop a proof of concept with at least two use cases relevant for predictive and personalised medicine in different medical fields to demonstrate the scientific added value compared to currently used methods and/or potential future clinical utility of the Generative AI models in biomedical research. The applicants should actively engage potential end users in the development, adaptation and testing of the new/repurposed models, considering sustainability aspects.
• Develop or revise existing methodologies to assess alignment with human values and the use cases of developed and/or repurposed Generative AI models, their applicability, performance, limitations and added value in biomedical research. These methodologies should demonstrate the technical, scientific, and potential future clinical utility, robustness and trustworthiness of the developed or repurposed Generative AI models, in particular:
  • Appropriate performance metrics for continuous evaluation and testing of scientific, technical robustness and relevance of the Generative AI models, as well as risks from misalignment of training data (which may degrade performance, e.g. through including but not limited to hallucinations or confabulations of these models).
  • Appropriate metrics for model intelligibility, robustness, alignment with ethical principles and approaches for ethical evaluation of AI trustworthiness[2].
  • Appropriate solutions to identify and mitigate potential bias and confounding[3] of Generative AI models and include examples from different perspectives (e.g., representativeness of the data, bias of the trainer, bias of training and validation data, algorithmic discrimination and bias including gender bias etc.).
  • Methods to systematically address and assess ELSI (Ethical, Legal, and Societal Implications) aspects, including data privacy, risk of discrimination/bias (not limited to sex, gender, age, disability, race or ethnicity, religion, belief, minority and/or vulnerable groups).
  • Appropriate techniques to ensure explainability of the model in order to increase users’ trust.
  • Approaches and metrics (where feasible) for the usability of Generative AI models for researchers.

All proposals should demonstrate EU added value by developing and/or using trustworthy and ethical Generative AI models developed in the EU and Associated countries, involving in the consortium EU industrial developers of Generative AI solutions, including leading-edge startups when possible. An open-source approach is encouraged when technically and economically feasible.

The proposals should adhere to the FAIR[4] data principles and apply GDPR[5] compliant processes for personal data protection based on good practices developed by the European research infrastructures, where relevant. The proposals should promote the highest standards of transparency and openness of models, as much as possible going well beyond documentation and extending to aspects such as assumptions, code and FAIR data management.

Proposals are encouraged to exploit potential synergies with other relevant projects funded under Horizon Europe and/or Digital Europe Programmes. When the use cases are relevant to diseases covered by specific Horizon Europe Partnerships or missions (e.g., the European Partnership on Rare Diseases, the Cancer Mission, etc.), the proposals should leverage the knowledge/data platforms already developed, such as the Virtual Platform of the European Joint Programme of Rare Diseases[6] etc. Moreover, the applicants are encouraged to leverage available and emerging European data infrastructures (e.g., the European Health Data Space[7], European Genomic Data Infrastructure[8], Cancer Image Europe[9], European Open Science Cloud[10], EBRAINS[11] etc.), whenever relevant. In addition, adopting EOSC recommendations and services for high-quality software is also encouraged, if applicable. The creation and expansion of health data and/or AI infrastructures or large-data curation initiatives, existing or under development, are not in the scope of this topic.

This topic requires the effective contribution of social sciences and humanities (SSH) disciplines and the involvement of SSH experts and institutions as well as the inclusion of relevant SSH expertise, in order to produce meaningful and significant effects enhancing the societal impact of the related research activities.

Successful proposals are encouraged to utilise the resources offered by the AI factories[12], when relevant and in accordance with the specific access terms and conditions.

Proposals should consider the involvement of the European Commission's Joint Research Centre (JRC) with respect to the value it could bring in providing an effective interface between research activities and pre-normative regulatory science as well as strategies and frameworks that address regulatory requirements. In that respect, the JRC will consider collaborating with any successful proposal and this collaboration, when relevant, should be established after the proposal’s approval.

All proposals selected for funding under this topic are strongly encouraged to collaborate, for example by participating in networking and joint activities, exchange of knowledge, developing, and adopting best practices, as appropriate. Therefore, proposals are expected to include a budget for the attendance to regular joint meetings and may consider covering the costs of any other potential joint activities without the prerequisite to detail concrete joint activities at this stage. The details of these joint activities will be defined during the grant agreement preparation phase.

AI Based Application Success Predictor

1. Scientific Excellence Is Paramount

For ERC grants, excellence is the sole selection criterion—evaluations focus exclusively on the quality of the research and track record .

Peer-reviewers adhere strictly to predefined criteria (e.g., Horizon ITN evaluations), and weaknesses—rather than strengths—often decide the outcome.

🌍 2. Strategic Alignment with EU Priorities

Horizon Europe emphasizes Green & Digital Transitions and resilience, with specific budget steering across biodiversity, climate, digital, and societal missions.

Proposals that clearly align with these strategic orientations and EU missions are significantly more competitive.

🤝 3. Strong, Diverse European Consortia

Horizon projects demand well-balanced consortia across Europe—geographically and disciplinarily diverse, including academia, industry, SMEs, NGOs .

Effective leadership, communication, trust, and active collaboration are key success factors .

🧴 4. Proven Research Infrastructure & Track Record

A strong publication record—especially in high-impact venues—and prior grant awards bolster chances .

ERC starting, consolidator, or advanced grants require exceptional citation records, strong proposals, and investigator track records .

📈 5. Robust Project Management

For large collaborative grants, project coordination, administration, and communication are just as crucial as scientific content .

Demonstrating realistic budget planning (100% direct costs + 25% indirect costs), administrative frameworks, and governance structures strengthens proposals .

💼 6. Fostering Mobility & Career Growth

Marie Skłodowska-Curie fellowships emphasize researcher mobility, interdisciplinary training, and developing future talent.

🧷 7. Geographical & Gender Equity

Northern and certain Eastern European institutions currently have higher success rates (≈22% vs below 18% in Southern Europe) .

ERC gender data: male and female applicants have similar success rates, though male applicants apply more frequently .

📌 Key Takeaways

🧭 Applicant Tips

Master criteria & avoid weaknesses: Make sure your proposal addresses common reviewer pitfalls—methodology, innovation, budget clarity.

Map to EU priorities: Explicitly connect your objectives to Horizon Europe’s strategic plan (2025–2027).

Build strong consortia early: Prioritize complementary expertise, geography, gender balance, and partner roles.

Show robust project management: Include a Work Package structure, governance plans, and clear communication strategies.

Leverage your track record: Highlight high-impact papers, leadership in projects, and previous awards.

Consider MSCA opportunities: Use them for mobility grants or integrating training into your project.

✅ In Summary

To maximize success with European Commission grants—especially ERC or Horizon Europe—focus relentlessly on scientific excellence, strategic EU alignment, consortium strength, and solid project planning. Combine these with a strong publication record and researcher development elements, and aim to close off any potential reviewer concerns.