AI-Powered Signal Detection in Pharmacovigilance

The EU funding opportunity known as HORIZON-JU-IHI-2025-11-03-two-stage focuses on enhancing pharmacovigilance through the use of Artificial Intelligence (AI) for signal detection and risk prediction related to medicinal products. This call is part of the Horizon Europe program, specifically under the Innovative Health Initiative (IHI) Joint Undertaking, and is categorized as a Research and Innovation Action (RIA). The expected total budget for this topic is approximately €8.9 million, with only one grant anticipated to be awarded.

Eligible applicants include universities, research organizations, small and medium-sized enterprises (SMEs), mid-sized companies, and patient organizations, while large companies may participate under specific conditions. Notably, entities from the UK and Canada are explicitly excluded from participation.

The funding mechanism involves monetary grants under Horizon Europe, with a mandatory co-funding requirement stating that at least 45% of project budgets must originate from industry or contributing partners, which can be in cash or in-kind contributions.

The application process is structured as a two-stage call. The first stage involves submitting a short proposal of up to 20 pages, due by October 9, 2025. Successful applicants will then be invited to submit a full proposal of up to 50 pages by April 29, 2026.

The target sectors for this initiative encompass healthcare, particularly in pharmacovigilance, regulatory science, and AI, focusing specifically on algorithm development and predictive analytics to improve drug safety and patient outcomes. The call encourages cross-sector collaboration, requiring the formation of consortia that include academic, industry, and healthcare stakeholders.

Projects must focus on developing AI algorithms for signal detection, validating various data sources such as electronic health records and spontaneous reporting systems, and identifying future risks through predictive modeling. Successful proposals must also align with ethical and legal standards regarding patient data and demonstrate how to implement AI in practical scenarios.

The initiative aims to foster advancements in the efficiency and accuracy of drug safety monitoring while establishing Europe as a leader in AI-driven pharmacovigilance. Clear guidelines for application submission, evaluation, and compliance with regulatory requirements are outlined, ensuring that proposed solutions are both trustworthy and effective in real-world applications.

The EU Funding Opportunity HORIZON-JU-IHI-2025-11-03-two-stage: AI-Powered Signal Detection in Pharmacovigilance is a call for proposals under the Horizon Europe program, specifically within the Innovative Health Initiative (IHI) Joint Undertaking. It is a Research and Innovation Action (RIA) with a two-stage submission process. The planned opening date is June 17, 2025, with deadlines for the first stage on October 9, 2025, and for the second stage on April 29, 2026. The total budget allocated to this topic is 8,906,000 EUR, and it is expected that one grant will be awarded.

The primary goal of this funding opportunity is to enhance drug safety through the application of Artificial Intelligence (AI) in pharmacovigilance, particularly in signal detection and risk prediction. The expected impacts include improved speed and accuracy in identifying adverse drug reactions, proactive risk management, enhanced patient safety, faster and more informed decision-making, increased efficiency in data processing, streamlined pharmacovigilance tasks, support for future policies and regulations, and increased consistency in approaches among industry, academia, and regulators.

The desired outcomes of this action are:

AI-powered algorithms and methods for faster and more accurate signal detection.

A comprehensive list of data sources where AI methods can improve signal detection, including recommendations and principles for a common data model to enable simultaneous analyses of various data sources (clinical trials, post-marketing surveillance).

AI-powered algorithms and methods for highly accurate risk prediction to identify potential future risks and enable proactive mitigation measures.

Recommendations, including practical considerations, for implementing AI-powered signal detection and risk prediction systems in real-world scenarios to ensure effective and trusted AI use.

Tools and templates for practical implementation of AI-powered signal detection and risk predictions by public and private stakeholders.

Training and user guides and other education materials on the implementation of the recommendations and the use of AI.

Transparency, trustworthiness, and adherence to ethical and legal principles regarding patient-level data and proprietary information are central to achieving these outcomes.

The scope of this funding opportunity focuses on using AI for signal detection and risk prediction in pharmacovigilance. It encompasses the evaluation, selection, optimization, and testing of AI algorithms using diverse data sources. It also includes evaluating various data sources within a cohesive pharmacovigilance network, developing predictive models for future risk identification, creating a recommendations document for implementing AI-powered systems, providing recommendations for human-in-the-loop (HITL) and human-on-the-loop (HOTL) AI in pharmacovigilance, developing templates and tools for practical implementation, and creating training plans and education materials for dissemination.

Specific activities to be funded include:

Evaluating, selecting, optimizing, and testing AI algorithms for signal detection using disparate data sources. This involves reviewing existing literature, selecting effective algorithms, pilot testing algorithms against different business scenarios, and optimizing AI algorithms for signal detection at the level of a medical concept or syndrome.

Evaluating diverse data sources for a cohesive pharmacovigilance network, including identifying data sources (EHRs, spontaneous reporting systems, social media, genomics), evaluating data quality and limitations, and developing recommendations for simultaneous analyses of different data sources.

Evaluating and developing predictive models to identify future risks, using results from signal detection and different data sources to forecast potential risks and enable proactive mitigation measures.

Developing a recommendations document for implementing AI-powered signal detection and risk prediction systems in real-world scenarios, including principles and practical considerations for effective, explainable, and trusted AI use, and engaging with the European Medicines Agency (EMA) for endorsement.

Developing recommendations for human-in-the-loop (HITL) and human-on-the-loop (HOTL) AI in pharmacovigilance signal detection for optimal performance and oversight.

Developing templates and tools for practical implementation, including integration into existing PV systems of AI-powered signal detection and risk prediction models by different stakeholders.

Developing training plans and education materials to disseminate the recommendations widely to the stakeholder community and develop a strategy for uptake.

Applicants are expected to adhere to ethical and legal principles, such as the Assessment List for Trustworthy Artificial Intelligence (ALTAI), and to develop a regulatory strategy and interaction plan for evidence generation. They should also foster proactive and early involvement of regional healthcare systems and health authorities.

The admissibility conditions include proposal page limits: 20 pages for RIA short proposals at stage 1 and 50 pages for RIA full proposals at stage 2. Eligible countries are described in Annex B of the Work Programme General Annexes, and specific provisions may apply to non-EU/non-Associated Countries. Other eligibility conditions are described in Annex B of the Work Programme General Annexes and in the IHI JU Work Programme. Financial and operational capacity and exclusion criteria are detailed in Annex C of the Work Programme General Annexes. Evaluation and award criteria, scoring, and thresholds are described in Annex D of the Work Programme General Annexes and the IHI JU Work Programme. Submission and evaluation processes are described in Annex F of the Work Programme General Annexes and the Online Manual. The indicative timeline for evaluation and grant agreement is also described in Annex F of the Work Programme General Annexes. Legal and financial set-up of the grants is described in Annex G of the Work Programme General Annexes.

Legal entities established in the UK and Canada are not eligible to receive funding under this topic.

Templates of reference documents and associated guidance can be found on the IHI JU website. Application forms, templates, and annexes are available in the submission system of the Funding and Tender Opportunities portal. Compulsory annexes include the Annex: Type of Participants and, for the second stage, the Annex to the budget and type of participants and the Annex: Declaration of in-kind contribution commitment. The Annex: Essential information for clinical studies is compulsory if the proposal includes clinical studies, otherwise a statement declaring the absence of clinical studies is required. The Annex: Ethics is optional.

The Model Grant Agreement is the HE General MGA v1.2. Additional documents include the Council Regulation (EU) 2021/2085, the IHI JU Work Programme (WP), the Strategic Research and Innovation Agenda (SRIA), the IHI JU Guide for Applicants, and IHI JU FAQs.

This funding opportunity aims to leverage the power of AI to revolutionize pharmacovigilance, making it faster, more accurate, and more proactive. It seeks to bring together industry, regulators, researchers, and healthcare providers to develop and implement AI-driven solutions that ultimately improve patient safety and public health. The emphasis on ethical considerations, regulatory compliance, and practical implementation ensures that the resulting AI systems are trustworthy, effective, and readily adopted by stakeholders. By fostering collaboration and providing clear guidelines, this initiative aims to establish Europe as a leader in AI-powered pharmacovigilance.

Eligible Applicant Types: The eligible applicant types are not explicitly stated, but based on the context, they likely include industry, regulators, researchers, academia, and other stakeholders involved in healthcare and AI. The call is for Research and Innovation Actions, suggesting that research organizations, universities, and companies with research and innovation capabilities are eligible.

Funding Type: The funding type is a grant, specifically a HORIZON Action Grant Budget-Based [HORIZON-AG], for Research and Innovation Actions (RIA).

Consortium Requirement: The opportunity appears to require a consortium, as it emphasizes collaboration among stakeholders and mentions the need for a common data model for simultaneous analyses of different data sources. The call also mentions the need for a 45% industry contribution, which implies a consortium with industry participation.

Beneficiary Scope (Geographic Eligibility): The geographic eligibility is likely aligned with Horizon Europe, which generally includes EU member states, associated countries, and potentially some third countries. However, legal entities established in the UK and Canada are explicitly ineligible for funding under this specific topic.

Target Sector: The target sector is health, specifically pharmacovigilance, with a focus on leveraging artificial intelligence (AI) for signal detection and risk prediction related to medicinal products. It also touches on ICT, specifically AI, machine learning, and predictive analytics.

Mentioned Countries: The United Kingdom and Canada are explicitly mentioned as countries whose legal entities are ineligible for funding. The European Union is implicitly mentioned as the funding body and target region.

Project Stage: The project stage is research and innovation, with an emphasis on development, validation, and demonstration. The call aims to evaluate, select, optimize, and test AI algorithms, develop predictive models, and create recommendations and tools for real-world implementation.

Funding Amount: The budget overview indicates that for the topic HORIZON-JU-IHI-2025-11-03-two-stage, which is AI-Powered Signal Detection in Pharmacovigilance, the contribution is around 8,906,000 EUR.

Application Type: The application type is a two-stage call, meaning applicants first submit a short proposal, and if successful, are invited to submit a full proposal.

Nature of Support: The beneficiaries will receive money in the form of a grant to support their research and innovation activities.

Application Stages: The application process involves two stages: a short proposal and a full proposal for those who pass the first stage.

Success Rates: The success rates are not explicitly mentioned, but the indicative number of grants for HORIZON-JU-IHI-2025-11-03-two-stage is 1.

Co-funding Requirement: The text mentions the need to comply with IHI additional eligibility criteria (e.g. 45% industry contribution), which implies a co-funding requirement, particularly from industry partners within the consortium.

Summary: This opportunity, HORIZON-JU-IHI-2025-11-03-two-stage, is a call for proposals under the Horizon Europe program, specifically targeting Research and Innovation Actions (RIA) in the field of pharmacovigilance. The goal is to enhance drug safety and patient outcomes by leveraging artificial intelligence (AI) for faster and more accurate signal detection and risk prediction related to medicinal products. The call encourages collaboration among industry, regulators, researchers, and other stakeholders to develop AI-powered algorithms, evaluate diverse data sources, create predictive models, and formulate recommendations for real-world implementation. The funding is provided as a grant, and the application process involves two stages: a short proposal followed by a full proposal for selected applicants. Consortia are required, and there is an emphasis on industry contribution, suggesting a co-funding element. Legal entities from the UK and Canada are ineligible for funding. The overall aim is to improve patient safety through earlier and more effective risk management, faster decision-making, and increased efficiency in pharmacovigilance processes, while also supporting future policies and regulations in the field. The indicative budget for the HORIZON-JU-IHI-2025-11-03-two-stage topic is around 8,906,000 EUR, and only one grant is expected to be awarded. The call aims to create a more consistent and evidence-based approach to pharmacovigilance by integrating AI technologies and fostering collaboration among various stakeholders.