Executive Summary

Artificial Intelligence (AI) is rapidly transforming drug and biological product development, offering opportunities for efficiency, innovation, and improved patient outcomes. However, regulatory compliance remains a key challenge for AI adoption in life sciences. The U.S. Food and Drug Administration (FDA) has issued guidance titled "Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products," outlining key principles and a risk-based credibility assessment framework. This whitepaper provides industry executives and stakeholders with a structured approach to understanding FDA guidelines, implementing AI strategies in compliance with regulatory expectations, and establishing robust governance frameworks.

Introduction

AI solutions are revolutionizing drug discovery, clinical trials, and manufacturing. However, with its increasing integration into the regulatory decision-making process, organizations must align their AI implementations with evolving regulatory guidelines.

While the FDA's AI guidance consists of non-binding recommendations rather than mandatory regulations, it underscores the importance of methodological transparency, data reliability, and a lifecycle management plan.

Failure to comply with these guidelines may lead to increased scrutiny during regulatory submissions and delays in approval. Additionally, adopting AI might encounter challenges that hinder its ability to enhance efficiency and speed up drug delivery to patients.

Proactively aligning with the FDAʼs recommendations enables organizations to streamline regulatory interactions, enhance AIʼs credibility, and drive successful implementation across the drug development lifecycle.

This whitepaper will help organizations:

• Interpret the FDA's AI guidance.
• Develop internal AI governance frameworks.
• Implement best practices for data management, risk assessment, and model validation.
• Ensure continuous AI model monitoring and assist in regulatory engagement.

Understanding the FDAʼs AI Guidelines

1 | Purpose and Scope of the FDA Guidance

The FDA guidance provides recommendations for sponsors, their development partners–including contract research organizations (CROs)- and technology providers on using AI models to support regulatory decision-making related to drug and biological products. The guidance covers nonclinical, clinical, postmarketing, and manufacturing phases, but not AI in early drug discovery.

Key areas of focus include:

• AI's role in generating regulatory data.
• AI's use across the drug product lifecycle (excluding drug discovery).
• The need for methodological transparency and reliability of AI-driven insights.

2 | AI Model Risk-Based Credibility Assessment Framework

The FDA proposes a structured approach to assess AI modelsʼ credibility based on a seven-step risk-based assessment framework:

Step 1 - Define the question of interest – Outlining the AI model's role.

The first step involves clearly identifying the specific question, decision, or concern that the AI model will address. By narrowing down the primary objective—such as predicting patient risk levels or assessing a critical quality attribute in manufacturing—sponsors establish the foundation for how AI outputs will be interpreted and applied.

Possible examples and use cases are:

• Generative AI: Define what content or insights the model should generate e.g., generating synthetic clinical data to supplement small patient populations.
• Computer Vision: Clarify the visual detection or classification task e.g., detecting micro-cracks in drug packaging.
• Natural Language Processing (NLP): Specify the language-based task e.g., summarizing free-text adverse event reports for pharmacovigilance.
• Reinforcement Learning: Optimizing dosage regimens in simulated trials.
• Predictive Modeling: Forecasting facility production capacity based on historical throughput data.

Step 2 - Define the context of use (COU) for the AI model – Recognizing factors influencing the models.

The sponsor specifies how and where the AI modelʼs outputs will be used. This includes clarifying whether the AI model is the sole source of information or one piece of a broader evidence package. The COU ensures all relevant stakeholders understand the scope of the AI model and how its findings factor into decision-making.

Here are a few examples to understand the COU:

• Generative AI: Will the generated content directly impact patient safety or labeling? Or will it be used in conjunction with human review? For example, AI-generated study summaries will be reviewed by clinical scientists and not used for direct patient decisions.
• Computer Vision: Is the model aiding quality control on production lines, or supporting clinical image evaluations? For instance, the AI solution serves as a first-pass detection tool for quality control, but humans make final decisions.
• NLP: Are model outputs used to screen clinical notes for eligibility in clinical trials, or merely assisting in drafting reports? For example, the AI solutions can flag potential adverse events from large volumes of clinical notes but final reporting requires human validation.
• Reinforcement Learning: Is the model used to refine protocols through simulations, or does it directly influence real-world decisions? For example, AI-driven simulations help optimize protocols, but final applications undergo safety reviews.
• Predictive Modeling: Is the model passively analyzing trends, or actively adjusting processes in real time? For instance, AI combines live sensor data with historical trends to monitor production line performance, but human oversight ensures final adjustments.

Step 3 - Assess AI modelʼs risk (based on model influence and decision consequences).

Sponsors evaluate two main factors: how influential the AI model will be in the decision and the potential severity of an incorrect outcome (decision consequence). When both influence and consequence are high, the model risk is high, requiring greater scrutiny of performance and reliability.

Examples:

• Generative AI: A model generating patient-facing content may pose higher risk if inaccurate data could affect treatment decisions; however, if it supports preliminary hypothesis generation, risk may be moderate/low.
• Computer Vision: Misclassifying critical anomalies (e.g., identifying contaminants in vials) can have high patient-safety implications. A model controlling automated product release without human review has high risk; a model assisting a human inspector has lower risk.
• NLP: Automated adverse event extraction from clinical notes might be high risk if it replaces manual checks for serious safety signals.
• Reinforcement Learning: Directly adjusting dosing in real time (high risk) vs. suggesting dose changes for researchers to consider (lower risk).
• Predictive Modeling: Forecasting batch quality in a continuous manufacturing line with direct product release decisions is high risk, while offering advisory output to a human operator is medium/low risk.

Step 4 - Plan to demonstrate AI model credibility

The fourth step outlines how the sponsor plans to demonstrate the AI modelʼs credibility. Sponsors describe the data and methods used to develop the model, explain any pre-trained components, detail how they will handle model bias, and propose performance metrics. This plan also addresses how the model will be tested and validated before real world deployment.

• Generative AI: Describe datasetsʼ curation approach, prompt engineering strategies (if applicable), and bias-mitigation methods including factual consistency checks.
• Computer Vision: Explain how images are collected, annotated, and used to train and evaluate detection or segmentation accuracy.
• NLP: Specify data selection process, text preprocessing steps, and performance metrics (e.g., precision, recall, F1).
• Predictive Modeling: Define how time-series, real-time sensor, or multi-omics data is handled; define thresholds for acceptable prediction error.

Step 5 - Execute the plan

Sponsors carry out the activities defined in the credibility assessment plan, including data collection, model training, and performance testing against independent datasets. This step often involves iterative refinement, where preliminary testing may reveal additional needs for data or alternative modeling approaches.

Possible example scenarios include:

• Generative AI: Perform iterative testing on generated outputs to ensure accuracy and alignment with regulatory needs, or perform controlled experiments to check for hallucinations or bias in synthetic text/data.
• Computer Vision: Collect new, real-world images to test robustness (e.g., various lighting, angles) and ensure consistent performance.
• NLP: Evaluate the modelʼs accuracy on real-world text data—clinical notes, adverse event forms, or labeling documents.
• Predictive Modeling: Conduct retrospective and prospective validation with real manufacturing or patient data to confirm forecast accuracy.

Step 6 - Document results and deviations

All outcomes of the credibility assessment plan, including any changes or deviations from the original plan, are recorded in a comprehensive report. This documentation should demonstrate how the sponsor addressed unanticipated challenges, what performance metrics were achieved, and whether the model met predefined acceptance criteria.

Possible examples are:

• Generative AI: Sample outputs, details on any modifications to the generative approach (e.g., prompt tuning), and error/failure modes.
• Computer Vision: Confusion matrices, error rates across different conditions, and justification for any re-training procedures.
• NLP: Summary of modelʼs language understanding performance (e.g., accuracy of entity extraction) along with any real-world test findings.
• Reinforcement Learning: Record of any modifications to the reward function or environment configuration after pilot studies.
• Predictive Modeling: Change log in hyperparameters or data preprocessing methods, along with final performance vs. acceptance criteria.

Step 7 - Determine AI model adequacy for its intended COU - Analyzing if the model meets its intended purpose.

Finally, based on the documented results, decide whether the AI model is suitable for its intended COU. If the modelʼs credibility is found insufficient for the proposed use, sponsors may refine the model, adjust its influence in decision-making, add additional sources of evidence, or choose a different modeling approach altogether.

Some examples are:

• Generative AI: If safety-critical text generation remains too unpredictable, limit the modelʼs role or add stronger validation checks.
• Computer Vision: If accuracy is inadequate under certain environmental conditions, institute additional QA processes or more conservative acceptance thresholds.
• NLP: If extraction of adverse events is prone to missing serious cases, refine data sources, re-train the model, or create human-in-the-loop review steps.
• Reinforcement Learning: If the modelʼs recommended actions are risky, incorporate stricter safety constraints or revert to a supervised learning approach.
• Predictive Modeling: If forecasting accuracy is too low for manufacturing release decisions, allow the model to flag potential issues for human evaluation instead.

3 | Lifecycle Management and Continuous Monitoring

The FDA emphasizes ongoing monitoring of AI models due to potential risks such as data drift, algorithmic bias, potential performance degradation, and unintended consequences.

Organizations must:

• Implement processes to update AI models, ensuring their relevance and effectiveness in a rapidly changing environment.
• Continuously evaluate AI performance.
• Document model changes and their impact on safety and regulatory compliance.

Strategic Implementation of AI in Regulatory Context

Building on the FDA's guidelines, this section outlines essential strategies for governance, data integrity, risk management, and regulatory engagement to support compliant and effective AI adoption.

1 | Establishing an AI Governance Framework

Organizations should create governance structures that align with the FDAʼs guidance, including:

• Defined roles and responsibilities for AI oversight.
• Policies to ensure compliance with FDA guidance.
• Processes for AI model validation and monitoring.

2 | Data Management and Model Validation

AI models must be trained on high-quality, unbiased, and representative datasets. Best practices include:

• Ensuring data accuracy, completeness, and traceability.
• Cleaning and validating data to ensure accuracy before training AI models.
• Documenting AI model development and training methodologies.

3 | AI Risk Assessment and Mitigation Strategies

Organizations should classify AI models based on risk:

• High-risk models (e.g., AI determining patient safety measures) require stringent validation.
• Low-risk models (e.g., AI used for workflow automation) need minimal oversight.

Risk mitigation strategies include:

• Human oversight in AI-driven decision-making.
• Leveraging industry-wide benchmarks to assess AI model performance by establishing a process that compares the AI solution's output against a gold standard, such as expert-derived manual results.
• Ensuring transparency in AI model reasoning and outputs to improve oversight, detect potential risks, and build trust.

4 | Documentation and Regulatory Engagement

Organizations should take the following steps to demonstrate model reliability and ensure traceability:

• Maintain detailed records of AI model development and validation.
• Engage with the FDA early in the AI implementation process.
• Ensure traceability of AI-generated regulatory data.

Best Practices for AI Adoption in Life Sciences

Building on the FDA's guidelines, this section outlines essential strategies for governance, data integrity, risk management, and regulatory engagement to support compliant and effective AI adoption.

1 | Common use cases

Identifying low-risk, high-value areas in your business, such as procurement, supply chain, and sales, provides an ideal starting point for organizations to experiment with AI and drive efficiency in these areas.

Take procurement, for example. AI can significantly reduce time, effort, and errors across various stages of the process:

• RFQ creation: Automating request-for-quote (RFQ) generation for faster and more accurate submission.
• Vendor qualification and onboarding: Streamlining supplier assessments and onboarding through AI-driven analysis.
• Query resolution: Accelerating response times by optimizing vendor interactions and reducing delays.
• Proposal evaluation: Ensuring unbiased, comprehensive, and swift comparison of vendor proposals, covering all RFQ terms.

As AI capabilities mature within an organization, expansion into more complex and regulated areas becomes possible, aligning with guidelines from authorities like the FDA. Some key AI-driven advancements in this space include:

• AI-powered clinical trial data analysis - Enhancing insights and decision-making in drug development.
• Predictive modeling for adverse drug reactions - Identifying potential risks earlier and improving patient safety.
• AI-driven pharmaceutical manufacturing - Automating production processes for greater precision and compliance.

Starting with operational efficiencies lays the groundwork for scaling AI into regulated domains while maintaining compliance and maximizing impact.

2 | Building Cross-Functional AI Teams

Successful AI implementation in life sciences requires collaboration across diverse expertise. Cross-functional teams bring together specialists from quality, manufacturing, clinical, regulatory, and other key areas to define problem statements clearly. These insights guide data scientists and AI engineers in developing solutions that address real-world challenges.

Once an AI solution is ready, functional experts play a critical role in validating its accuracy, efficacy, and overall fit for purpose. This iterative approach ensures AI innovations are not only technically sound but also practical and impactful in real-world applications.

3 | Ethical AI and Transparency

Responsible integration of AI must prioritize ethics from the beginning. Proactively addressing bias, transparency, and accountability builds trust and ensures compliance.

Key considerations include:

• Embed transparency from the start:
  - Understand how AI models make decisions—know the data sources, algorithms, and key assumptions.
  - Identify and address potential biases before deployment through rigorous testing.
  - Conduct regular audits to ensure AI remains fair, compliant, and aligned with business goals.
• Design AI for fairness and accountability:
  - Use diverse, high-quality datasets to reduce bias and improve accuracy.
  - Define clear ownership—who is responsible for AI decisions, and how are errors corrected?
  - Establish governance frameworks to track AI performance and ensure ongoing oversight.
• Prioritize data privacy and security:
  - Collect only the data necessary for AI applications, avoiding unnecessary risks.
  - Implement strict data governance policies, including encryption and access controls.
  - Continuously monitor and update security protocols to meet evolving regulatory standards.
• Create a culture of ethical AI:
  - Train teams to recognize ethical risks and integrate responsible AI practices.
  - Encourage cross-functional collaboration to ensure AI fairly serves all stakeholders.
  - Keep human oversight central—AI should enhance decision-making, not replace it.

By addressing these considerations early, you can build AI solutions that are not only innovative but also ethical, trustworthy, and aligned with regulatory and business standards.

Roadmap for Organizations to Implement AI in Compliance with FDA Guidelines

Short-Term Actions (0-6 months)

• Identify high-value use cases - Prioritize AI initiatives that drive measurable business impact.
• Build AI readiness - Evaluate your data and systems to ensure they can support your AI use cases effectively.
• Deliver POCs with clear ROI – Prove value fast and lay the groundwork for scalable AI adoption.

Mid-Term Actions (6-18 months)

• Develop an internal AI governance policy.
• Engage with the FDA for early regulatory discussions.
• Implement risk assessment protocols for AI models.
• Establish data management best practices.
• Conduct AI model validation studies aligned with FDA recommendations.

Long-Term Actions (18+ months)

• Integrate AI-driven solutions into regulatory decision-making workflows.
• Develop AI lifecycle monitoring processes.
• Continuously update AI models in response to new FDA regulations.

Conclusion

AI offers transformative potential for drug development and regulatory decision-making. However, organizations must proactively align AI adoption with FDA guidelines to ensure regulatory compliance, mitigate risks, and unlock AI's full potential.

Call to Action:

• Prioritize AI governance and compliance strategies.
• Invest in AI model transparency and lifecycle management.
• Engage and align with the FDA early to avoid regulatory roadblocks.

By adopting a structured approach, life sciences companies can harness AI's benefits while ensuring regulatory alignment and ethical AI practices.