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Challenge

Generative AI Medical Device Regulatory Compliance Checker with AutoGen

FDA regulatory oversight for generative AI medical devices demands strict compliance auditing of model risk controls and lifecycle logs. You will build an AutoGen multi-agent system featuring regulatory auditor and medical device engineer agents to verify AI clearance documentation with 92% regulatory alignment.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Design an AutoGen multi-agent debate and validation loop to audit generative AI medical device submissions against FDA regulatory frameworks.

Delivery guide

How work is evaluated

Evaluation

Evaluates regulatory compliance deficiency detection accuracy in SaMD documentation.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

gap_identification_check

Ensure critical regulatory gaps are detected

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

compliance_precision

Precision of identified FDA regulatory non-compliance gaps • target: 0.92 • range: 0-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • Implement multi-agent conversation systems using Microsoft AutoGen

  • Create specialized conversable agents for FDA AI/ML medical device guidance

  • Automate verification of AI drift controls, risk mitigation plans, and software logs

Resources and assets

Reference links and supporting material

Dataset notes

Dataset of 100 generative AI medical device software documentation files with annotated regulatory non-compliance flags.

Challenge setup

Evaluates regulatory compliance deficiency detection accuracy in SaMD documentation.

Challenge input

JSON object with document_text string

AutoGen

Mandatory Microsoft AutoGen framework.

Evaluated output

JSON object with is_compliant boolean and identified_gaps list

Evaluation checks
  • Ensure critical regulatory gaps are detected
  • Precision of identified FDA regulatory non-compliance gaps • target: 0.92 • range: 0-1
Configured evaluation evidence
  • Benchmark: RegAI-Bench
  • Compliance Precision target: 0.92
  • 1 public reference case
Configured runtime
  • 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

Action Space
  • AutoGen · Required
  • Aim · Optional
  • Kombai · Optional

Evaluation contract

  • gap_identification_check · Weight 1
  • compliance_precision · Weight 1

Metrics

  • compliance_precision · Target 0.92

Benchmarks

  • RegAI-Bench
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON object with document_text string

    Output: JSON object with is_compliant boolean and identified_gaps list

    Execution harness: Python

Evaluation tools

  • AutoGen · Multi-Agent Framework

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Generative AI Medical Device Regulatory Compliance Checker with AutoGen