Hi everyone,

I’m organizing a small team project for an AI/healthcare innovation competition focused on oncology molecular data interoperability and reconciliation.

Our proposed project is:

OncoReconcile AI

An AI-assisted platform designed to standardize and reconcile oncology genomic information across:

• VCF files
• molecular pathology PDF reports
• vendor-specific biomarker formats
• structured clinical/genomic data

The goal is to transform fragmented molecular oncology data into explainable, standardized, and interoperable outputs that could support:

• molecular tumor board workflows
• cohort generation
• downstream analytics
• clinical research
• interoperability pipelines

Current Technical Direction

We are exploring a hybrid architecture combining:

• HGNC gene normalization
• HGVS variant normalization
• ontology-grounded mappings
• biomedical NLP / entity extraction
• LLM-assisted reconciliation
• explainable confidence scoring
• human-in-the-loop review workflows

Potential standards/tools under evaluation include:

• HL7 FHIR / mCODE
• ClinVar / ClinGen
• HGVS
• BioBERT / SciSpacy
• RAG-based architectures

Current MVP Scope

To keep the project realistic for a small team and limited timeline, we are likely focusing on:

• NSCLC initially
• a limited hotspot gene set (EGFR, KRAS, ALK, BRAF, etc.)
• 2–3 molecular vendor formats
• PDF + VCF reconciliation workflows

Feedback We Are Looking For

We would greatly appreciate feedback from people working in:

• oncology informatics
• molecular pathology
• bioinformatics
• clinical genomics
• healthcare interoperability
• biomedical NLP
• precision medicine platforms

Especially around:

1. Common real-world reconciliation pain points
2. Vendor-specific genomic reporting inconsistencies
3. Explainability and validation expectations
4. Existing open-source tools/frameworks we should evaluate
5. Clinical workflow considerations we may overlook
6. FHIR/mCODE/genomics interoperability best practices
7. Public datasets suitable for realistic MVP development

We are intentionally positioning this as:

• AI-assisted,
• explainable,
• standards-aligned,
• human-reviewed,

rather than fully autonomous interpretation.

Thanks in advance for any guidance, references, or suggestions.