Validating Generative AI: A Practical Framework for Reliability and Compliance in Life Sciences

Have you ever asked a generative AI tool a question and received an answer that sounded plausible but turned out wrong? Generative AI models occasionally “hallucinate,” creating convincing yet entirely fabricated answers. This underscores why validation matters when integrating these platforms.  
 
Generative AI promises significant benefits, from automating routine tasks to enhancing decision-making. However, without rigorous validation, your organization risks using tools that produce unreliable, inaccurate, or ethically questionable results. 

One framework gaining traction for validating generative AI systems is Computer Software Assurance. 

Why Computer Software Assurance (CSA)? 

Traditional computer system validation often drowns teams in excessive documentation. Computer Software Assurance (CSA) prioritizes validation activities based on their risk, which can significantly reduce unnecessary documentation and overhead costs. 

The core concepts of CSA include: 

• Digitalization: Automating validation tasks to reduce human errors and increase speed and efficiency.
• Data integrity and analytics: Real-time data analytics enable continuous verification and faster detection of AI model deviations that can disrupt your business. 
• Agility: Any validation strategies must adapt to rapid technological, regulatory, and market shifts. 
• Collaboration: Establishes clear communication across departments and with regulatory bodies to simplify the validation process. 

CSA is particularly effective for validating the target areas most critical to operational safety and regulatory compliance. It encourages a mix of automated and hands-on testing to make sure your AI meets accuracy, reliability, and ethical standards. 

One of CSA’s core ideas is continuous verification, regularly checking that your AI still works the way it should, even as your data or use cases change. This keeps your systems dependable and effective, no matter how fast technology moves. 

Assessments for Generative AI 

Generative AI uses patterns from existing data to create new, original content such as text, images, or other outputs. While powerful, these tools require thorough validation. Here are metrics your organization can use to validate these platforms: 

Perplexity and log-likelihood: These are “surprise detectors.” If AI is surprised by what comes next, it’s probably not smart. Lower perplexity means AI is better at predicting what words should come next. Log-likelihood is similar: The higher it is, the more confident AI is in its responses. 

BLEU and ROUGE scores: How well does AI’s writing match what a human would say? BLEU and ROUGE measure that, specifically for tasks like summarization or translation.  

• BLEU (Bilingual Evaluation Understudy) checks if AI is using the right words in the right order, relative to human-written content. A higher BLEU score means the AI-generated text more closely resembles the human-written content. 
• ROUGE (Recall-Oriented Understudy for Gisting Evaluation) focuses primarily on recall. It checks if AI is hitting the important points. A high ROUGE score indicates that the AI model effectively captures key content from the human-generated reference. 

Together, these metrics help ensure that generative AI produces content that aligns well with human standards for accuracy and clarity—that it’s writing something that is useful and relevant. 

Inception Score (IS) and Frechet Inception Distance (FID): These scores ask whether AI-generated images look real and whether they are unique. IS rewards images that are realistic and clear, as well as how varied and unique they are. The higher the IS, the better. FID looks at features like colors, shapes, and textures of AI-generated images to see how closely they resemble real ones. A lower FID score indicates the generated images more closely resemble real-world images. 

Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR): These are visual quality checks for AI-created images. For example, SSIM measures how similar a generated image is to an original reference image. It looks at structure, including edges, textures, and patterns. If the score is close to 1, it’s doing a great job. PSNR assesses image quality by measuring how much noise or distortion is in the image, comparing it pixel by pixel. A higher score means the image is cleaner and more accurate. 

Balancing Technical with Human Feedback 

To validate generative AI, you need a well-rounded approach. The above metrics show how well AI is performing behind the scenes. But just as important is having real people review the results. They ensure the generated content is useful, accurate, and makes sense in the real world. Human reviewers catch things machines might miss and help make sure that AI’s results meet your users’ needs. 

AI Validation Challenges 

Validating generative AI isn’t without hurdles. One major challenge is explainability. Complex AI models often operate like “black boxes,” meaning it’s hard to understand how they make decisions. That can make it tough to trust or act on their results. 

Data quality is another major hurdle. If the data is messy, biased, or incomplete, the AI’s output will be, too. That leads to results that are inaccurate, skewed, or unethical. 

Then there’s scalability. Validating AI across all the ways your company might use it takes time and resources. It’s important to do this thoroughly, but it can slow down development unless you find smart ways to scale the process. 

Finally, environments that constantly change—like in life sciences—make things even more complicated. New regulations or medical discoveries can quickly make existing models outdated. If you’re not continuously updating and revalidating your AI, you risk not just falling out of compliance but making decisions based on inaccurate data, which can have serious consequences, especially when patient care is involved. 

Navigating Regulatory Guidelines 

The FDA’s recent draft guidance encourages a risk-based approach to evaluating AI. It recommends organizations assess how well an AI model performs and how risky it might be based on its purpose and use. 
 
A key point in the new guidance is a focus on maintaining AI over time. Because AI models can shift as new data comes in or environments change, the FDA wants organizations to keep a close eye on performance throughout a model’s life. That way, it stays accurate and reliable, especially in critical areas like drug development. 

The FDA also supports early and open conversations between companies and regulators about how AI will be developed and validated. This collaboration brings AI into the regulatory process without compromising safety or quality. 

Overall, the FDA’s guidance aligns closely with CSA principles and encourages a structured, ongoing approach to validation that makes compliance easier and AI more trustworthy. 

Real-Life AI Success Stories  

Successful AI implementation is happening today across highly regulated industries like life sciences and pharmaceuticals. Companies are using AI not just for streamlining operations, but also to improve quality and compliance while reducing risk. Here are some examples: 

• Predictive maintenance: By analyzing equipment data in real-time, AI helps organizations anticipate failures before they occur. One drug manufacturer used this approach to cut unplanned downtime by 20% and boost consistency across product output. 
• Process optimization: Machine learning, an AI algorithm, can fine-tune variables to reduce inefficiencies. One global life sciences organization applied AI to vaccine production and achieved a 50% reduction in process variability. 
• Real-time quality monitoring: AI systems can track critical quality indicators throughout production, alerting teams to deviations immediately. One multinational healthcare company reduced quality issues by 35% when using a real-time AI solution. 
• Regulatory compliance: Natural Language Processing (NLP) tools streamline document analysis and compliance reviews. An innovative medicines company adopted AI to manage regulatory documents more efficiently, cutting related workloads by 30%. 

With the right approach for validating generative AI and the right implementation partner, companies can uncover similar gains without compromising on quality, compliance, or control. 

Your Path to Validated AI with Sikich 

Sikich can guide your team through every validation stage, applying proven CSA methodologies and industry best practices. 

Ready to confidently leverage AI? Contact Sikich today to start your journey.