KCS RESEARCH PROJECT
GenDAI was conceived as an integrated medical-diagnostics platform that connects consented microbiome samples, automated metagenomic processing, secure cloud infrastructure, AI-based biomarker discovery and interactive clinical reporting. Its purpose is to accelerate the conversion of research and technology into personalised diagnostic services while respecting laboratory and regulatory requirements.
Explore the project architectureMicrobiome research can generate valuable evidence, but clinical translation requires more than an accurate model. Samples, metadata, processing pipelines, biomarkers, access controls, visual reporting, validation and regulatory evidence must operate as one traceable diagnostic system.
The questions connect the real-world problem with research activities and evaluable contributions.
The GenDAI architecture connects clinical inputs, metagenomic processing, secure data services, AI-supported discovery, reporting and validation as one diagnostic pathway.
IBD cohort
Stool samples
Clinical metadata
Quality control
Sequence processing
Traceable data products
INTEGRATED GENDAI PLATFORM
Secure access
Reproducible processing
Long-term archiving
Biomarker identification
Sequence classification
Personalised profiles
Visual analysis
Clinical report
Decision-oriented interface
Patient assessment
Monitoring
Marketable tool suite
The architecture begins with patients, informed consent, clinical requirements and microbiome samples. The automated diagnostics workflow converts raw metagenomic material into quality-controlled and traceable data products. These products enter the integrated GenDAI platform, where GenDAI Safe governs cloud data, security, reproducibility and archiving; GenDAI Discovery uses AI to identify biomarkers and classify sequences; and Interactive Reporting turns analytical outputs into interpretable clinical views. Piloting and validation measure performance across the complete chain. Regulatory evidence, ethics and quality management therefore surround the architecture rather than appearing only at the end. The intended result is a validated diagnostic capability that supports personalised assessment and monitoring.
Table 1 translates the proposed platform into six measurable objectives. The mid-term and final targets make progress reviewable across clinical data, automation, infrastructure, AI performance, reporting and regulation.
| Objective | Platform result | Purpose | Selected KPI | M18 | M36 |
|---|---|---|---|---|---|
| RIO 1 | Clinical dataset | Consented IBD patient microbiome samples | Patients / samples | 100 | 2,000 |
| RIO 2 | Diagnostics workflow | Fully automated metagenomic processing | Manual minutes per sample | 10 | 5 |
| RIO 3 | GenDAI Safe | Secure, reproducible cloud data and knowledge infrastructure | OpenSSF scorecard | 7.5/10 | 8.5/10 |
| RIO 4 | GenDAI Discovery | AI biomarker discovery and metagenomic classification | Fine-tuned classification accuracy | 75% | 85% |
| RIO 5 | Interactive Reporting | Visual analysis and clinical reporting | Processing time / error rate | 180 s / 5% | 30 s / 1% |
| RIO 6 | Clinical translation | Marketable and regulatory-compliant tool suite | IVDR compliance | — | 100% |
Table 1: Selected GenDAI Research and Innovation Objectives and milestone targets.
The objectives deliberately combine different kinds of evidence. RIO 1 measures whether the clinical dataset reaches sufficient scale. RIO 2 tests whether the workflow reduces manual processing. RIO 3 evaluates software and access-policy quality, while RIO 4 measures the improvement of AI classification. RIO 5 examines speed, failure rate and the clinician’s cognitive workload. RIO 6 brings the strands together by requiring the integrated technology to produce the evidence needed for regulatory-compliant operation.
CONSORTIUM CONTRIBUTION
Prof. Philippe Tamla contributed to the GenDAI consortium through January 2026. The interdisciplinary work connected clinical requirements, metagenomic processing, cloud and data infrastructure, artificial intelligence, interactive reporting, integration and validation within one research and innovation programme.
His experience includes connecting research objectives, work packages, system architecture and measurable indicators while considering the transition from research outputs to usable, regulated technology. GenDAI’s results remain the collective work of the consortium.
Each workstream addresses a distinct part of the project while remaining connected to the shared architecture and questions.
Create consented metagenomic datasets from patients with inflammatory bowel disease and preserve the clinical and ethical context of every sample.
Integrate a largely automated data-processing pipeline that reduces manual effort while keeping sample and quality controls explicit.
Provide secure cloud-based data and knowledge infrastructure for reproducibility, controlled access and long-term archiving.
Develop AI methods for biomarker identification, sequence classification and personalised microbiome profiling.
Translate analytical results into visual clinical reports that reduce processing time, errors and cognitive workload.
Validate the integrated system and assemble evidence needed for marketability and compliance with the In Vitro Diagnostic Regulation.
ENGAGE WITH KCS
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CONTACT / APPOINTMENTS
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