FDA AI Risk Assessment

Executive Summary

ClinShield is designed in accordance with the FDA's guidance document "Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products" (January 2025). This risk assessment demonstrates how our hybrid architecture—combining deterministic rule-based checking with optional AI-assisted quality analysis—aligns with the FDA's risk-based framework for AI credibility assessment.

1. Question of Interest (FDA Step 1)

Primary Question:

"Does this clinical trial protocol comply with ICH E6 (R2) Good Clinical Practice requirements?"

ClinShield evaluates clinical trial protocols against established regulatory standards, identifying structural gaps, missing content, and areas requiring sponsor attention before regulatory submission.

2. Context of Use (FDA Step 2)

Dimension	Description
Role	Pre-submission protocol compliance analysis
Scope	Identifies structural and content gaps in protocols before regulatory filing
Use Case	Advisory tool for sponsors during protocol development
Decision Authority	Final compliance decisions remain with sponsor and regulatory authorities

ClinShield operates exclusively in the pre-submission phase, providing sponsors with actionable insights to strengthen their protocols before formal regulatory review. The tool does not make regulatory decisions and is not involved in patient-facing applications.

3. AI Model Risk Assessment (FDA Step 3)

ClinShield employs a hybrid architecture with two distinct layers, each carrying different risk profiles:

1 Layer 1: Deterministic Checking

Factor	Assessment
Technology	Rule-based pattern matching and keyword detection
Function	Identifies missing sections and required content
AI Risk Level	LOW — No machine learning; fully explainable
Model Influence	HIGH — Primary source of compliance findings
Decision Consequence	MEDIUM — Incorrect finding results in unnecessary sponsor review
Overall Risk	MEDIUM

This layer provides deterministic, reproducible results with complete transparency into the evaluation logic.

2 Layer 2: AI Quality Analysis (Optional)

Factor	Assessment
Technology	Large Language Model (LLM API)
Function	Grades content quality and provides improvement recommendations
AI Risk Level	MEDIUM — Uses ML with probabilistic outputs
Model Influence	LOW — Advisory only; sponsor retains decision authority
Decision Consequence	LOW — Incorrect advice can be disregarded by sponsor
Overall Risk	LOW

The AI layer enhances analysis but does not override deterministic findings or make autonomous decisions.

4. Credibility Assessment Plan (FDA Step 4)

Deterministic Layer Credibility

Data Source: ICH E6 (R2) requirements codified as structured rules
Validation: Manual verification against source regulatory documents
Evidence: Complete search chains documenting sections checked, keywords attempted, and pages reviewed
Transparency: 100% reproducible and auditable results

AI Layer Credibility

Model: Anthropic Claude (current production version)
Training: Pre-trained foundation model with no custom fine-tuning on proprietary data
Evaluation: Human expert review of AI-generated recommendations
Monitoring: Output quality assessment on representative protocol samples
Transparency: Evidence chains documenting AI reasoning and source references

5. Risk Mitigation Strategy

Built-in Controls

Human-in-the-Loop: All findings require sponsor review before action
Multiple Evidence Layers: Deterministic checking, AI analysis, and expert review operate as complementary verification
Complete Transparency: Evidence chains provided for every finding, enabling full auditability
No Direct Patient Exposure: Pre-submission use only; no involvement in patient care or treatment decisions
Regulatory Oversight Preserved: FDA independently reviews all submitted protocols

Lifecycle Maintenance

Requirements Database: Version-controlled, updated with new regulatory guidance
Model Monitoring: Ongoing accuracy tracking on representative protocol samples
User Feedback Integration: Continuous improvement based on sponsor input and regulatory developments

6. Regulatory Positioning

ClinShield Is Designed For:

Pre-submission quality control and gap analysis
Internal protocol optimization during development
Reducing submission delays through early identification of deficiencies
Supporting regulatory strategy and submission readiness

ClinShield Is NOT:

A replacement for regulatory review by FDA or other authorities
A substitute for expert clinical or regulatory judgment
A guarantee of FDA approval or favorable regulatory outcome
A patient-facing, diagnostic, or therapeutic tool

7. Compliance Statement

ClinShield's architecture aligns with the FDA's risk-based framework for AI credibility assessment:

Clear Context of Use: Pre-submission review with defined scope and limitations
Appropriate Risk Assessment: Hybrid approach separates deterministic and AI components with distinct risk profiles
Transparent Credibility Evidence: Complete audit trails and evidence chains for all findings
Human Decision Authority: Advisory tool only; sponsors and regulators retain full decision-making control

This design minimizes AI-related regulatory risk while maximizing value for protocol development and submission readiness.