Build a Google ADK & OpenAI o3 Multi-Cancer Diagnostic Utility Agent
Multi-Cancer Early Detection (MCED) blood tests generate complex real-world data requiring clear diagnostic utility mapping and patient counseling. Build a Google Agent Development Kit (ADK) agent powered by OpenAI o3 reasoning to evaluate patient MCED test results and generate clinical action plans.
What you are building
Construct an agent using Google ADK and OpenAI o3 reasoning to interpret MCED test outcomes and generate evidence-based downstream workups.
How work is evaluated
Evaluates agent ability to generate appropriate follow-up diagnostic pathways from MCED signals.
Datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
correct_imaging_modality
Checks if contrast-enhanced CT or EUS is included for pancreatic tissue of origin.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
guideline_conformance_score
Conformity score against standard NCCN workup protocols for suspected malignancies. • target: 0.95 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Learning goals
Orchestrate clinical workflow agents using Google Agent Development Kit (ADK)
Leverage OpenAI o3 complex reasoning capabilities to weigh tissue of origin (TOO) probabilities
Formulate standardized diagnostic workup recommendations based on NCCN guidelines
Generate patient-facing plain language explanations that prevent over-diagnosis confusion
Resources
Synthetic dataset of 30 MCED blood test patient results, covering positive and negative signals across multiple organ systems.
Challenge setup
Evaluates agent ability to generate appropriate follow-up diagnostic pathways from MCED signals.
Challenge input
JSON containing signal_detected (boolean), predicted_too (string), and patient_age (int).
Google ADK
Agent development kit framework for modular agent architecture.
OpenAI o3
High-capacity reasoning engine for clinical logic.
Evaluated output
JSON with recommended_workup (list), recommended_imaging (list), and clinical_summary (string).
- Checks if contrast-enhanced CT or EUS is included for pancreatic tissue of origin.
- Conformity score against standard NCCN workup protocols for suspected malignancies. • target: 0.95 • range:...
- Benchmark: NCCN Diagnostic Pathway Benchmark
- Guideline Conformance Score target: 0.95
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
Shows the first task, up to three tools, and up to three evidence items.
View technical recipe
Configured tools
- Google · Required
- Google ADK · Optional
- o3 · Optional
Evaluation contract
- correct_imaging_modality · Weight 1
- guideline_conformance_score · Weight 1
Metrics
- guideline_conformance_score · Target 0.95
Benchmarks
- NCCN Diagnostic Pathway Benchmark
- biotech
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON containing signal_detected (boolean), predicted_too (string), and patient_age (int).
Output: JSON with recommended_workup (list), recommended_imaging (list), and clinical_summary (string).
Execution harness: Python
Evaluation tools
- Google ADK · Agent Framework
- OpenAI o3 · Reasoning LLM
Recipe freeze marker
No recipe freeze time is recorded.