FDA-Compliant AI: Executive Guide

The U.S. Food and Drug Administration's 2025 draft guidance on artificial intelligence for drug and biological product development represents a watershed moment for pharmaceutical innovation. For the first time, the FDA has articulated a comprehensive framework for how sponsors should approach AI in regulatory contexts. This guidance—while explicitly non-binding—signals the agency's evolving expectations and provides pharmaceutical executives with a blueprint for responsible AI adoption.

The pharmaceutical AI market is projected to explode from $4 billion in 2025 to $25.7 billion by 2030, a 542% increase. Yet many initiatives remain stuck in pilot purgatory because they weren't designed with regulatory compliance in mind from the start. The disconnect stems from treating FDA compliance as a final validation step rather than a foundational design principle.

What Does the FDA's AI Guidance Mean for Pharmaceutical Executives?

The FDA's draft guidance establishes a framework that fundamentally reshapes how pharmaceutical companies should approach AI deployment. Understanding the strategic implications—not just technical requirements—is essential for executives leading AI transformation.

The Guidance Is Non-Binding But Signals Future Expectations

The FDA explicitly states the guidance represents the agency's "current thinking" rather than binding regulatory requirements. As Pravartan Technologies' CEO notes: "While these recommendations are not binding, they represent the FDA's evolving thinking and signal what regulators will look for in AI-based submissions. Organizations that treat this as a blueprint for responsible innovation will be better positioned for success."

Pharmaceutical executives should interpret non-binding status as an invitation to engage proactively with the FDA. Companies that implement the framework's principles now will shape regulatory expectations through their submissions.

Four Core Principles Permeate the Framework

The FDA guidance emphasizes four principles that should inform every AI initiative:

• Transparency in how AI models are designed, validated, and deployed
• Reliability and traceability of data used throughout the AI lifecycle
• Continuous monitoring to detect drift, bias, or performance degradation
• Contextual risk assessment based on the model's role and influence

These principles reflect the FDA's fundamental concern: ensuring that AI systems used to support regulatory decisions are trustworthy and fit for their intended purpose.

How Do You Implement the FDA's 7-Step Credibility Framework?

The FDA's seven-step credibility framework provides a structured approach to ensuring AI systems are trustworthy and fit for use in pharmaceutical development.

Step 1: Define the Question of Interest with Precision

The framework begins by requiring clear articulation of what the AI model is intended to support. Vague problem statements like "improve drug discovery" are insufficient. The FDA expects precision: specific predictions, accuracy thresholds, and performance metrics.

Precise problem definition enables appropriate model selection, validation strategy design, and regulatory review. It forces organizations to clarify how AI outputs will be used in decision-making and what level of accuracy is required for the intended purpose.

Step 2: Clarify the Context and Constraints

Context clarification requires understanding how the AI system fits within existing workflows, regulatory frameworks, and decision-making processes. This includes:

• Identifying all stakeholders who will interact with or be affected by the AI system
• Understanding data sources, quality, and representativeness
• Recognizing constraints specific to the therapeutic area and regulatory pathway
• Defining the role of human oversight and intervention
• Establishing performance requirements based on intended use

Step 3: Assess the Risk Systematically

Risk assessment evaluates potential harms from AI system failures or incorrect predictions. In life sciences, risks range from patient safety concerns to regulatory non-compliance to business impacts. The FDA expects organizations to:

• Identify potential failure modes and their consequences
• Assess likelihood and severity of each risk
• Implement mitigation strategies proportionate to identified risks
• Define monitoring processes to detect emerging risks
• Establish escalation procedures when risks materialize

Step 4: Create a Comprehensive Plan

Planning encompasses technical approach, validation strategy, documentation requirements, and governance frameworks. The FDA expects plans to address:

• Data quality assessment and preprocessing approaches
• Model selection rationale and training methodology
• Performance metrics aligned with intended use
• Validation strategy including internal and external datasets
• Documentation requirements for regulatory review
• Ongoing monitoring and maintenance procedures

Step 5: Execute and Validate Rigorously

Execution requires rigorous validation demonstrating that AI systems perform as intended across relevant conditions. This includes:

• Internal validation on held-out test sets
• External validation on independent datasets from different sources
• Prospective validation in real-world settings when appropriate
• Subgroup analysis to detect performance variations
• Stress testing with edge cases and adversarial examples
• Comparison to existing approaches or human performance

Step 6: Document Everything Transparently

Documentation requirements for AI systems exceed traditional software documentation. The FDA expects comprehensive records of:

• Data sources, preprocessing, and quality assessment
• Model architecture, training methodology, and hyperparameters
• Validation results across multiple datasets and conditions
• Limitations, assumptions, and known failure modes
• Governance processes and decision-making authority
• Change management procedures for model updates

This transparency enables regulatory review and builds confidence in AI-assisted decisions. As Domino Data Lab's VP emphasizes: "The FDA is asking for transparency and traceability across the AI lifecycle, and that's exactly what modern AI platforms provide."

Step 7: Assess Suitability Continuously

The final step requires ongoing assessment of whether the AI system remains suitable for its intended purpose. This includes:

• Monitoring performance metrics in production
• Detecting data drift and concept drift
• Evaluating whether assumptions remain valid
• Identifying new edge cases or failure modes
• Updating systems as needed or retiring them when unsuitable
• Documenting all changes and reassessments

What Organizational Capabilities Enable FDA-Compliant AI?

Implementing the FDA framework requires organizational capabilities beyond technical AI expertise.

Critical organizational capabilities include:

• Cross-functional teams combining data science, regulatory, clinical, and quality expertise
• AI platforms providing transparency and traceability by design
• Governance frameworks defining roles, responsibilities, and decision authority
• Documentation systems capturing AI lifecycle from development through deployment
• Continuous monitoring infrastructure detecting performance degradation
• Change management processes for model updates and revalidation
• Regulatory engagement strategies for proactive FDA interaction

How Should Pharmaceutical Companies Engage with FDA on AI?

Proactive FDA engagement creates competitive advantages for pharmaceutical companies deploying AI.

Effective engagement strategies include:

• Pre-submission meetings to discuss AI approaches before major investments
• Type C meetings for specific AI methodology questions
• Participation in FDA workshops and public comment periods
• Sharing case studies demonstrating successful AI implementation
• Building relationships with FDA reviewers familiar with AI
• Contributing to industry working groups shaping AI standards

What Are the Common Pitfalls in FDA-Compliant AI Deployment?

Pharmaceutical organizations often encounter predictable challenges:

• Treating FDA compliance as final validation step rather than design principle
• Insufficient documentation during AI development requiring expensive retrofitting
• Inadequate data quality assessment and preprocessing
• Validation limited to internal datasets without external validation
• Lack of continuous monitoring infrastructure for production AI systems
• Unclear governance frameworks and decision-making authority
• Insufficient cross-functional collaboration between AI and regulatory teams
• Passive approach to FDA engagement waiting for final binding guidance

How Do Leading Pharmaceutical Companies Approach FDA-Compliant AI?

Leading pharmaceutical companies share common patterns in FDA-compliant AI deployment:

• Embed FDA principles into AI system design from day one
• Invest in AI platforms providing transparency and traceability by design
• Establish cross-functional governance combining technical and regulatory expertise
• Engage proactively with FDA through pre-submission meetings and workshops
• Build comprehensive documentation systems capturing full AI lifecycle
• Implement continuous monitoring infrastructure for production systems
• Treat compliance as competitive advantage rather than burden
• Share learnings with industry to shape evolving regulatory expectations