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Out-of-Distribution-aware Reasoning Agents for Clinical Learning and Exploration

Cel

We have recently observed an unexpected and profoundly promising synergy: multi-modal out-of-distribution detection methods, originally developed for normative modelling in the ERC MIA-NORMAL project, appear to endow AI agents with remarkable autonomy in real-world scientific reasoning. By explicitly encoding uncertainty and recognising unfamiliar inputs, these agents become capable of detecting anomalies, and they begin to act independently: curating data, identifying patterns, and proposing hypotheses in complex clinical domains. This breakthrough reframes the role of AI in medicine. No longer confined to fixed-task classification or retrospective association, agentic AI systems can now act as autonomous collaborators in medical discovery. We validated this concept in cardiovascular
imaging, where agents independently conducted phenome-wide association studies, rediscovered known correlations, and surfaced novel phenotype-disease links with transparent, explainable logic.
With ORACLE, we aim to extend this paradigm to oncology, focusing on pancreatic cancer, a devastating disease with limited early detection and poor outcomes. Agents will begin with large-scale, multimodal clinical data: radiology, pathology, molecular reports, and labs, and autonomously curate and structure it into patient trajectories. Once organised, agents will explore the data landscape:
clustering phenotypes, linking imaging to molecular patterns, and testing hypotheses across cohorts. When normative data is available, they compare against population-wide baselines; when it is not, agents can use our generative methods to impute missing values and identify structure.
Running entirely on real clinical data within GDPR-compliant infrastructure, ORACLE will demonstrate that autonomous AI agents can produce publishable, clinically relevant insights. This PoC may not only advance cancer research but reshape how biomedical discovery is done: transforming it into an automated, data-driven process.

Dziedzina nauki (EuroSciVoc)

Projekt nie został jeszcze sklasyfikowany według klasyfikacji EuroSciVoc.
Wskaż dziedziny nauki, które twoim zdaniem są najbardziej istotne z punktu widzenia tego projektu i pomóż nam usprawnić naszą usługę klasyfikacji.

Program(-y)

Temat(-y)

System finansowania

HORIZON-ERC-POC - HORIZON ERC Proof of Concept Grants

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Zaproszenie do składania wniosków

(odnośnik otworzy się w nowym oknie) ERC-2026-POC

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Instytucja przyjmująca

FRIEDRICH-ALEXANDER-UNIVERSITAET ERLANGEN-NUERNBERG
Wkład UE netto
€ 150 000,00
Adres
FREYESLEBENSTRAßE 1
91058 Erlangen
Niemcy
Region
Bayern Mittelfranken Erlangen, Kreisfreie Stadt
Rodzaj działalności
Higher or Secondary Education Establishments
Linki
Koszt całkowity
Brak danych

Beneficjenci (1)