MCED Pre-test Counseling and Risk Stratification with AI SDK
Rising consumer interest in multi-cancer early detection testing demands structured pre-test risk assessment and patient guidance. Develop a web-based clinical decision agent using Vercel AI SDK and Moonshot AI to evaluate patient risk criteria and generate tailored clinical summaries. Achieve 100% compliance with standard NCCN screening guidelines.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop an interactive MCED risk stratification agent using Vercel AI SDK and Moonshot AI for patient pre-test counseling.
How work is evaluated
Evaluates guideline compliance and clinical accuracy of generated MCED pre-test counseling summaries.
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.
guideline_compliance_check
Requires compliance score of 1.0 against NCCN rules.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
guideline_compliance
Adherence to established oncology risk stratification standards • target: 1 • 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
Implement interactive AI streaming interfaces using Vercel AI SDK
Utilize Moonshot AI models for clear, empathetic clinical text generation
Implement rule-based NCCN guideline validation checks during multi-turn interactions
Format structured risk assessments for integration into Electronic Health Record portals
Reference links and supporting material
Synthetic dataset of 100 patient clinical profiles covering diverse age ranges, risk profiles, and cancer screening histories.
Challenge setup
Evaluates guideline compliance and clinical accuracy of generated MCED pre-test counseling summaries.
Challenge input
JSON containing patient age, family history, smoking history, and prior cancer diagnoses.
AI SDK
Required framework for building streaming user-facing AI applications.
Moonshot AI
Specified language model engine for nuanced patient communication.
Evaluated output
JSON object with recommended_for_mced boolean, guideline_compliance_score, and patient_summary text.
- Requires compliance score of 1.0 against NCCN rules.
- Adherence to established oncology risk stratification standards • target: 1 • range: 0-1
- Benchmark: NCCN Guideline Adherence Benchmark
- Guideline Compliance target: 1
- 1 public reference case
- JavaScript execution harness
- javascript 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
- Moonshot AI · Required
- Guidance · Optional
- Vercel AI SDK · Optional
- Vercel AI SDK · Optional
Evaluation contract
- guideline_compliance_check · Weight 1
- guideline_compliance · Weight 1
Metrics
- guideline_compliance · Target 1
Benchmarks
- NCCN Guideline Adherence Benchmark
- biotech
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON containing patient age, family history, smoking history, and prior cancer diagnoses.
Output: JSON object with recommended_for_mced boolean, guideline_compliance_score, and patient_summary text.
Execution harness: JavaScript
Evaluation tools
- AI SDK · TypeScript Streaming Framework
- Moonshot AI · LLM Provider Engine
Recipe freeze marker
No recipe freeze time is recorded.