LlamaIndex MCED Diagnostic Utility RAG with GPT-5 Pro
Multi-cancer early detection (MCED) tests produce complex biomarker reports requiring precise patient-facing context and clinical guidelines. Implement an agentic RAG system using LlamaIndex and GPT-5 Pro to map MCED blood signals to evidence-based diagnostic pathways. Achieve zero hallucinated clinical recommendations on test cases.
What you are building
The core problem, expected build, and operating context for this challenge.
Build an advanced RAG and agent workflow using LlamaIndex with GPT-5 Pro to map multi-cancer early detection signals to clinical follow-up protocols.
How work is evaluated
Evaluates the factual fidelity and clinical precision of RAG generated diagnostic follow-up recommendations.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
zero_hallucinated_citations
Verifies that all returned citations match indexed source guidelines.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
guideline_compliance
Percentage of correct clinical diagnostic steps suggested. • target: 0.95 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Construct a LlamaIndex VectorStoreIndex over MCED clinical practice guidelines
Integrate GPT-5 Pro model for zero-shot RAG synthesis with strict citation matching
Design custom LlamaIndex RouterQueryEngine to route patient inquiries to diagnostic sub-indices
Implement automated clinical factual consistency guardrails
Reference links and supporting material
Collection of public MCED clinical consensus guidelines, NCCN pathways, and simulated patient test report cases.
Challenge setup
Evaluates the factual fidelity and clinical precision of RAG generated diagnostic follow-up recommendations.
Challenge input
JSON with patient_age, mced_signal_tissue_of_origin, biomarker_level
LlamaIndex
Required framework for document indexing and retrieval routing.
GPT-5 Pro
Provides state-of-the-art context processing and clinical analysis.
Evaluated output
JSON containing recommended_imaging, specialist_referral, and citations
- Verifies that all returned citations match indexed source guidelines.
- Percentage of correct clinical diagnostic steps suggested. • target: 0.95 • range: 0-1
- Benchmark: ClinicalRAG-Bench
- Guideline Compliance target: 0.95
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
- GPT-5 Pro · Required
- Llama Index · Optional
- GPT-5 · Optional
Evaluation contract
- zero_hallucinated_citations · Weight 1
- guideline_compliance · Weight 1
Metrics
- guideline_compliance · Target 0.95
Benchmarks
- ClinicalRAG-Bench
- biotech
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON with patient_age, mced_signal_tissue_of_origin, biomarker_level
Output: JSON containing recommended_imaging, specialist_referral, and citations
Execution harness: Python
Evaluation tools
- LlamaIndex · RAG and agent data framework
- GPT-5 Pro · LLM Provider
Recipe freeze marker
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