Clinical AI for Early Detection and Biomarker-Based Prescription

Context

Clinical laboratories generate a high volume of data that is often underutilized for identifying undiagnosed conditions or anticipating complications. This challenge seeks solutions capable of predicting and/or prescribing based on biomarkers obtained from clinical analyses, supporting early diagnosis and optimization of therapeutic interventions.

Objectives of the Challenge

·         Identify undetected pathologies or imminent clinical risks.

·         Propose additional diagnostic tests or personalized therapeutic adjustments.

·         Ensure the model's explainability, safety, and robustness.

·         Regulatory preparation in accordance with CE marking under MDR, if applicable.

·         Integration with LIS/HIS/EHR systems via HL7 v2/FHIR.

Type of Technology Sought

·         AI/ML/DL, NLP, time series analysis, hybrid models.

·         Big Data: data quality, pipelines, MLOps, version control.

·         Optional hardware if it provides differential biomarkers.

Partner Profiles

Startups, technology centres, universities, and scale-ups with experience in digital health, MDR regulations, ISO 13485, ISO 14971, IEC 62304, ISO 27001, and GDPR. Maturity level TRL 6–8.

Scope of the Challenge

·         Proof of concept lasting 8–12 weeks with light integration and initial clinical validation.

·         Optional pilot in a real-world setting.

·         Deliverables: functional demo, validation report, regulatory dossier, integration and clinical adoption plan.

Keywords

AI in health, biomarkers, clinical analyses, early diagnosis, assisted prescription, explainable AI, SaMD, CE, MDR, HL7, FHIR, MLOps.