Add to favorites:
Share:
This funding call aims to leverage multimodal data to advance the application of Generative Artificial Intelligence (AI) in biomedical research. With a focus on predictive and personalized medicine, it supports projects that develop and repurpose robust Generative AI models, utilizing large-scale, complex, and high-quality biomedical data. Selected proposals will deliver proof-of-concept use cases in predictive medicine, methodologies for evaluating AI models' ethical and scientific rigor, and innovative solutions for addressing biases. Proposals must adhere to the FAIR data principles and encourage EU-led AI innovations, fostering synergies with Horizon Europe and Digital Europe Programmes. Emphasizing interdisciplinary approaches, the call requires the participation of academia, industry, and healthcare professionals to ensure sustainable and impactful outcomes for a healthier society.
Opening: 22-05-2025
Deadline(s): 18-09-2025
Data provided by Kooperationsstelle Wissenschaft
This funding opportunity represents a pre-agreed draft that has not yet been officially approved by the European Commission. The final, approved version is expected to be published in the first quarter of 2025. This draft is provided for informational purposes and may be used to preliminarily form consortia and develop project ideas, but it is offered without any guarantees or warranties.
Expected Outcome
• Access to robust and ethical Generative AI models for biomedical research.
• Enhanced understanding and application of AI in synthesizing multimodal data.
• Methodologies to ensure transparency, validity, and explainability of AI models.
• Solutions for mitigating biases and ensuring equitable AI applications.
• Increased trust and usability of Generative AI in biomedical research.
Scope
• Develop or repurpose Generative AI models using large-scale, multimodal biomedical data.
• Demonstrate use cases for predictive and personalized medicine.
• Evaluate the performance, ethical alignment, and applicability of AI models.
• Address societal and ethical implications, including biases and data privacy.
• Foster collaboration across industry, academia, and healthcare sectors.