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AI-BOOST Challenge 3: Generative AI for Enhancement of Clinical Datasets

Description

This challenge aims to develop a Generative AI solution that enhances clinical imaging datasets by generating synthetic patient cohorts, completing missing clinical and imaging data, and improving dataset balance and harmonisation. The objective is to create higher-quality datasets for training and validating AI models, particularly for underrepresented patient populations, thereby reducing bias and improving the generalisability of AI-based diagnostic and predictive tools in cancer care.

Funding Amount

The AI-BOOST competition follows a two-stage prize model.

SPARK Phase

  • 5 winners receive €28,500 each.
  • Prize paid in two instalments:
    • 50% after signing the Participation Agreement.
    • 50% after successfully completing the Mid-Term Checkpoint.

ADVANCE Phase

  • The five SPARK winners continue into the Advance Phase.
  • One overall Challenge Winner receives €100,000.

Special Awards

  • Innovation Excellence Award: €12,500
  • Responsible AI Award: €12,500

(A participant may receive both the Challenge Prize and a Special Award.)

Submission Deadlines

  • Call opens: 3 July 2026
  • Application deadline: 8 September 2026 (17:00 CEST)

Submission Conditions

Applications must be submitted through the AI-BOOST F6S platform by 8 September 2026 (17:00 CEST). Proposals must address the Challenge 3 objectives and comply with the AI-BOOST programme requirements.

  • Applications must be completed in English.
  • Only one application per applicant is permitted.
  • Solutions must focus on synthetic clinical data generation, dataset enhancement and imaging harmonisation.
  • Projects must comply with Responsible AI, ethical and EU legal requirements, including the EUCAIM data usage conditions.
  • Development must take place within the secure processing environment provided for the challenge.
  • Selected participants must complete the SPARK and ADVANCE phases, mentoring activities, Mid-Term Checkpoint and Final Live Competition.

Eligible Businesses

The competition is open to organisations and innovators capable of developing AI solutions for healthcare and medical imaging, including:

  • SMEs and start-ups
  • Large companies
  • Universities and higher education institutions
  • Research organisations
  • NGOs and non-profit organisations
  • Individual innovators
  • Single legal entities
  • Consortia

Eligible Countries

Applicants must be established in:

  • EU Member States
  • Horizon Europe Associated Countries
  • EU Overseas Countries and Territories (OCTs)

Technology Readiness Level (TRL)

  • Expected starting TRL (competition level): TRL 2–3
  • Target TRL for Challenge 3: TRL 7
  • The challenge aims to deliver a prototype validated for integration into the EUCAIM infrastructure for clinical AI model development.

Expected Outcome

Projects are expected to deliver:

  • A Generative AI model for synthetic clinical data generation.
  • A tool identifying the key variables driving synthetic cohort generation.
  • Enhanced and augmented clinical datasets.
  • A validation report assessing data fidelity, realism, consistency and bias reduction.
  • A prototype capable of improving existing EUCAIM cohorts and generating new synthetic cohorts for AI model training and validation.
  • Integration of the prototype into the EUCAIM infrastructure with recognition of the developers and opportunities for future exploitation.

Scope

Projects should develop a Generative AI solution capable of improving the quality and representativeness of clinical datasets used for medical AI.

The solution should be able to:

  • Generate synthetic patient cohorts from existing clinical and imaging data.
  • Identify the key demographic, clinical and imaging variables driving cohort generation.
  • Augment existing datasets to improve representation of underrepresented patient groups.
  • Complete missing clinical variables and imaging acquisitions.
  • Harmonise imaging data acquired under different protocols.
  • Evaluate the quality, realism and consistency of the generated datasets against the original data distribution.

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