---
title: How will FDA’s new AI draft guidance impact Drug Development?
description: FDA AI Draft Guidance
---

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# [How will FDA’s new AI draft guidance impact Drug Development?](https://blog.inovia.bio/inovia-bio-insights/how-will-the-fda-new-ai-draft-guidance-impact-drug-development)

 Written by [Antonio Nicolae](https://blog.inovia.bio/inovia-bio-insights/author/antonio-nicolae) | 29-Jan-2025 15:46:42

The [**FDA’s draft guidance** on the use of **artificial intelligence (AI) in regulatory decision-making** for drugs and biologics ](https://www.fda.gov/regulatory-information/search-fda-guidance-documents/considerations-use-artificial-intelligence-support-regulatory-decision-making-drug-and-biological)represents a key step in defining how AI can be integrated into the drug development and regulatory landscape. The document lays out a **structured, risk-based credibility assessment framework** that companies must follow when submitting AI-generated evidence to the FDA. This approach is designed to ensure that AI models used in regulatory submissions are **trustworthy, transparent, and appropriately validated**.

 

### **The FDA’s AI Credibility Framework: A Seven-Step Process**

The guidance provides a **seven-step risk-based framework** to assess the credibility of AI models used to support regulatory decision-making. These steps serve as a structured process for AI developers and pharmaceutical companies to validate their AI models.

> **1. Defining the Question of Interest**
> 
> - The AI model's role and purpose must be clearly articulated, particularly in the context of drug development and regulatory decision-making.
> - AI should be used to answer specific, well-defined regulatory questions (e.g., “Can AI help stratify patient risk in a clinical trial?”).

 

> **2. Defining the Context of Use (COU)**
> 
> - The **specific role and scope** of an AI model within the regulatory process must be established.
> - Companies must specify whether the AI model is used **alone or alongside human judgment or other data sources** in decision-making.
> - Example: If an AI model is used to **predict patient safety risks in a trial**, the COU should define the **data sources, model function, and how its output is integrated into the clinical workflow**.

 

> **3. Assessing AI Model Risk**
> 
> - The FDA proposes a **risk matrix** based on two primary factors: 
>     - **Model Influence** – How heavily the AI model’s outputs are weighted in regulatory decision-making.
>     - **Decision Consequence** – The potential **impact of an incorrect AI decision** on patient safety, drug efficacy, or product quality.
> - High-risk models (e.g., AI models making **direct safety assessments for trial participants**) will require **more rigorous validation** than lower-risk applications (e.g., AI models optimizing administrative processes in drug development).

 

> **4. Developing a Credibility Assessment Plan**
> 
> - AI developers must create a **formal validation plan** detailing: 
>     - **How the AI model was built**
>     - **What data was used**
>     - **How biases were minimized**
>     - **How model performance will be evaluated over time**
> - The plan should be **tailored to the AI model’s risk level**, with **higher-risk models requiring more stringent validation and oversight**.

 

> **5. Executing the Plan**
> 
> - Companies must implement the validation plan **as part of their AI deployment**.
> - AI models should be rigorously tested and refined based on the **predefined credibility assessment strategy**.

 

> **6. Documenting Outcomes & Deviations**
> 
> - AI models must maintain **detailed records** of their performance, including: 
>     - **Training data sources**
>     - **Bias mitigation strategies**
>     - **Any deviations from the original validation plan**
> - Transparency is a key requirement; companies must demonstrate that **their AI models remain reliable over time**.

 

> **7. Determining AI Model Adequacy**
> 
> - AI developers must **periodically reassess** whether the model remains fit for purpose.
> - Any **changes in data inputs or clinical context** must trigger a **re-evaluation of AI credibility**.

 

### **Key Challenges & Considerations Highlighted in the Guidance**

While AI holds enormous potential for accelerating drug development, the FDA identifies **several risks** that companies must address:

> ##### **Data Quality & Bias Concerns**
> 
> - AI models rely heavily on training data, but **biases in datasets** (e.g., underrepresentation of certain patient groups) can lead to **flawed conclusions**.
> - The FDA emphasizes that AI models must be trained on **representative, high-quality data** that aligns with the regulatory **"fit for use"** standard.

> ##### **Model Explainability & Transparency**
> 
> - The **lack of interpretability** in some AI models, particularly deep learning models, presents regulatory challenges.
> - The FDA expects companies to **clearly document how AI models reach conclusions** and provide mechanisms for **human oversight**.

> ##### **Lifecycle Maintenance & AI Model Drift**
> 
> - AI models must be **continuously monitored** to ensure that they **remain accurate and do not drift** when exposed to new data.
> - The FDA requires companies to implement **ongoing model validation processes** to detect performance degradation.

> ##### **Early FDA Engagement: A Must for AI Success**
> 
> The FDA is actively encouraging sponsors to consult early when using AI, offering engagement opportunities through:
> 
> - **Complex Innovative Trial Design (CID) Program** – For integrating AI into novel trial designs.
> - **Emerging Drug Safety Technology Program (EDSTP)** – Focused on AI in post-marketing safety monitoring.
> - **Real-World Evidence (RWE) Program** – Evaluating AI applications in real-world data analysis.
> 
> **Early alignment with the FDA is critical** to ensuring AI models meet regulatory expectations and avoid late-stage rejection.

 

### **What will this mean for Drug Developers & AI Vendors**

Although the guidance is currently in draft form, it indicates an evolving, **mature regulatory approach to AI**, where **high-risk AI models will require formal validation and transparency** before being accepted in regulatory decision-making. Companies using AI in drug development should take the following steps:

- **Establish Robust AI Governance:**
  
    - Implement **internal frameworks for AI validation, documentation, and continuous oversight**.
    - Adopt **best practices for AI model risk assessment and bias mitigation**.
- **Align AI Model Use with Regulatory Expectations:**
  
    - AI applications in **clinical trials, safety assessments, and manufacturing** will face **higher regulatory scrutiny**.
    - Companies should tailor AI deployment strategies to align with **FDA credibility standards**.
- **Engage Regulators Early:**
  
    - AI model developers should proactively **consult the FDA** before using AI for regulatory submissions.
    - Companies should leverage the FDA’s risk framework to **demonstrate compliance and build trust in AI-driven decisions**.

 

### **Conclusion: A New Era of AI Oversight in Pharma**

The **FDA’s draft guidance sets a precedent for AI adoption in drug development, balancing innovation with patient safety and product integrity.** Rather than restricting AI’s role, the agency is providing **a structured approach** to ensure AI models used in regulatory decision-making are **credible, transparent, and fit for purpose.**

Especially for AI vendors, this is a **wake-up call**—AI must go beyond the hype curve and be accurate and** credible**. Pharma and Biotechnology organizations that **prioritize robust validation, proactive regulatory engagement, and ongoing monitoring** will be best optimally positioned to **integrate AI effectively into drug development and regulatory processes.**

This guidance also signals that **AI will continue to play a critical role in advancing drug development**—but with a growing emphasis on **governance, lifecycle management, and human oversight.** Companies that align their AI strategies with the **FDA’s risk-based framework** will not only **accelerate regulatory acceptance** but also **enhance the trust and reliability of AI-driven insights.**

[View full post](https://blog.inovia.bio/inovia-bio-insights/how-will-the-fda-new-ai-draft-guidance-impact-drug-development)

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