Why This Guide Matters

Choosing the right AI solution for clinical development isn't just about checking boxes - it's about finding a partner who understands the complexities of drug development and can deliver reliable, compliant solutions. This guide will help you evaluate vendors through the lens of regulatory requirements, technical capabilities, and practical implementation.

Classification

• 80-100: Recommended for Scientific Drug Development Tasks

• 60-79: Recommended for Administrative Tasks

• <60: Not Recommended

Mandatory Requirements for Clinical Development use-cases:

Must score 1 in:

• FDA Alignment Framework

• Hallucination Prevention

• Data Protection

• Risk Management

• Use Case Specialization

The AI vendor scoring calculator explained:

The upcoming sections provide a detailed explanation of how the AI Vendor risk calculator functions and outline the considerations a Biotechnology company should take into account when using it.

1. Regulatory Compliance & AI Credibility

Understanding FDA Alignment

This section is crucial because the FDA's AI draft guidance framework provides a structured approach to validating AI in drug development. 

Key questions to ask:

• How does the vendor implement the FDA's seven-step credibility framework?
  • What's their process for defining research questions?
  • How do they establish and document use cases?
  • What's their approach to risk assessment?
  • How do they validate their models?
  • What's their documentation process?
  • How do they handle model updates?
• Can they show examples of successful regulatory submissions or have they engaged with regulators such as the FDA?

For more information on the FDA's draft, please read our blog post on it: https://blog.inovia.bio/inovia-bio-insights/how-will-the-fda-new-ai-draft-guidance-impact-drug-development

Risk Management

Understanding risk is fundamental - different AI applications carry different levels of risk to patient safety and drug development outcomes.

Look for:

• Clear categorization of AI applications by risk level
• Documentation of potential impacts on patient safety
• Risk mitigation strategies for high-stakes applications
• Regular risk assessments and updates

Data Quality & Bias

The quality of AI outputs depends heavily on the training data - this is especially important in clinical development where patient diversity matters.

Evaluate:

• Data source documentation
• Strategies for identifying and addressing bias
• Inclusion of diverse patient populations
• Data quality control processes

2. Technical Robustness & Transparency

Model Explainability

In clinical development, being able to understand and explain AI decisions isn't optional - it's essential for regulatory compliance and scientific validity.

Check for:

• Clear documentation of model decision-making
• Ability to trace outcomes back to inputs
• Human oversight mechanisms
• Transparency in model architecture

Hallucination Prevention

AI hallucinations in clinical development can have serious consequences - comprehensive automated testing and strong safeguards are essential.

Key features:

• Robust automated testing infrastructure (aim for 1000s of tests running multiple times daily)
• Multi-level alerting system:
  • Real-time alerts for critical hallucinations affecting patient safety
  • Automated notifications for pattern-based hallucinations
  • Escalation protocols based on severity
• Edge case detection through specialized test suites targeting domain-specific scenarios
• Real-time monitoring systems flagging potential hallucinations for expert review
• A continuous feedback loop where every caught hallucination leads to new test cases
• Clear documentation of hallucination rates and detection methods
• Transparent protocols for handling and reporting suspected hallucinations
• Regular validation by domain experts to verify AI outputs against known scientific facts

3. Maintenance & Validation

Performance Monitoring

AI models can drift over time, potentially compromising their reliability. Continuous monitoring is crucial.

Look for:

• Regular performance assessments
• Drift detection systems
• Update protocols
• Version control and documentation

Governance

Strong governance ensures consistent quality and compliance.

Essential elements:

• Use of version control tools - such as git
• Audit trails
• Clear roles and responsibilities
• Regular reviews and updates

4. Practical Implementation

Use Case Specialization

Avoid vendors who claim their AI can do everything - specialization matters in clinical development.

Consider:

• A clear distinction between scientific and administrative applications
• Proven expertise in specific areas
• Realistic scope of capabilities
• Track record in similar applications

Clinical Development Workflow Integration

The best AI solution is one that fits seamlessly into your existing processes.

Evaluate:

• Integration capabilities
• Training requirements
• Support systems
• Performance metrics

5. Security & Compliance

Data Protection

Clinical data requires the highest level of security.

Verify:

• Regulatory compliance (GDPR, HIPAA)
• Security measures
• Protection against data poisoning
• Incident response protocols

6. Vendor Credibility

Experience & Expertise

Past performance in clinical development is a strong indicator of future success.

Look for:

• AI & Industry expertise
• Case studies
• Client references
• Regulatory track record

Decision Framework:

The following section explains the scoring matrix for the AI Vendor reliability calculator and how it reaches certain outcomes.

Score each item:

• 0 = Not implemented/Inadequate

• 1 = Fully implemented/Adequate

Scoring Categories

Regulatory Compliance & AI Credibility (Max: 15 points)

Technical Robustness & Transparency (Max: 30 points)

Maintenance & Validation (Max: 25 points)

Practical Implementation (Max: 20 points)

Security & Compliance (Max: 5 points)

Vendor Credibility (Max: 5 points)

Maximum Score: 100 points

Classification

• 80-100: Recommended for Scientific Drug Development Tasks

• 60-79: Recommended for Administrative Tasks

• <60: Not Recommended

Mandatory Requirements for Clinical Development use-cases:

Must score 1 in:

• FDA Alignment Framework

• Hallucination Prevention

• Data Protection

• Risk Management

• Use Case Specialization

Making the Final Call

• For regulatory-influencing applications, all "Must Have" criteria should be met
• For non-regulatory applications, focus on security and maintenance
• When in doubt, prioritize vendors with strong clinical development experience

Remember: The right vendor isn't necessarily the one who can market their AI the loudest  - it's the one that best understands and addresses your specific clinical development challenges while maintaining compliance and safety.