Is this an AI system?

In regulated industries such as life sciences, decision-making systems have always played a critical role in ensuring compliance, quality, and patient safety. Historically, expert systems were widely used to support structured decision-making. Today, however, artificial intelligence (AI) systems are reshaping how organizations analyze data, generate insights, and automate processes. 

For GxP-regulated environments, understanding what constitutes an AI system is essential for building effective validation strategies, mitigating risks adequately, and ensuring regulatory compliance. 

This article explores the differences between expert systems and AI systems, their applications, and what this evolution means for organizations operating under Good Practice framework (GxP). 

What Is an Expert System? 

An expert system is a rule-based software system designed to replicate the decision-making ability of a human expert. It relies on a predefined set of if–then rules and a structured knowledge base [1]. 

Key Characteristics: 

• Based on fixed, human-defined rules  

• Deterministic and predictable outputs  

• No inherent learning capability  

• Requires manual updates to incorporate new knowledge   
  

Typical GxP use cases include deviation classification, batch release decision trees, CAPA process workflow and equipment troubleshooting.  

What Is an AI System? 

Artificial intelligence systems go beyond static rules. They are designed to learn from data, adapt over time, and improve their performance without explicit reprogramming [1]. 

Key Characteristics: 

• Learns from historical and real-time data  

• Adapts and evolves over time  

• Can handle complex, non-linear relationships  

• May produce probabilistic (not deterministic) outputs

A table is worth a 100 words…   

Dimension	Expert Systems	AI Systems
Logic Foundation	Rule-based (if-then)	Data-driven
Adaptability	Static	Dynamic and learning
Transparency	High	Variable (can be opaque)
Validation Approach	Traditional CSV	Requires enhanced AI validation frameworks

In essence, expert systems codify what we already know, while AI systems uncover patterns we may not yet understand, and use those patterns to produce their outputs.  

AI Models Matter 

AI encompasses multiple families of models that have their own areas of specializations.  It is important to know those models exist.  First, to understand them, and secondly, to build a strong validation strategy for them as each model introduces different considerations and risk profiles.    

Key models include: 

Model Type	Use Case	Examples in clinical research
Descriptive	Describe and summarize existing data	Dashboards, descriptive analysis of test data, performance indicators
Diagnostic	Analyze data to explain a situation or problem	Identification of causes of adverse events, analysis of trends or discrepancies
Predictive	Anticipate future results from data	Prediction of patient risks, probability of recruitment or abandonment
Prescriptive	Recommend actions to take	Protocol optimization, clinical or operational recommendations
Generative	Create content   (text, data, code).	Report writing, synthetic data generation, draft protocols
Conversational	Interact with users in natural language	Support chatbots, assistants for searching for clinical information

Implications for GxP Compliance 

The shift from expert systems to AI introduces both opportunities and challenges in regulated environments. 

1. Validation Complexity 

Expert systems are relatively straightforward to validate because they work from explicit rules or logic, making their outputs predictable.   

AI systems, however, may evolve over time (continuous learning), and have probabilistic outcomes; meaning that the same inputs, or prompts, can lead to different outputs.  

This means AI systems come with validation challenges on their own, including defining strategies for: 

• Model training and testing documentation  

• Performance monitoring over time  

• Data integrity and bias assessment  

It is important to mention that systems built on AI models can be prevented from evolving on their own (Frozen VS Continuous Learning Models).  AI systems using frozen learning models remain probabilistic in nature, but they do not run the risk of seeing their AI model drift over time, at the cost of preventing self-improvements from taking place.   

2. Data Integrity and Governance 

AI systems are only as reliable as the data they are trained on. In a GxP context, this raises critical questions about the data being used: 

• Is it legally usable?  

• Is it representative and unbiased?  

• Is it traceable and auditable?  
  

Robust data governance frameworks become a prerequisite for AI adoption, aligned with regulatory expectations [2]. 

3. Explainability and Auditability 

Regulators expect decisions to be explainable. While it is more straightforward for expert systems to meet this requirement, especially when complete system specifications are available, AI systems (especially advanced models) can be less transparent. 

To meet this expectation, organizations working from critical AI systems must often create explainability tools (e.g., model interpretability techniques), clear documentation of model logic and limitations and risk-based justifications for model use. 

When to Use Expert Systems vs AI Systems 

Expert Systems Are Best When: 

• The process is well understood  

• Rules are stable and rarely change  

• High transparency is required  

• Regulatory scrutiny is high and tolerance for uncertainty is low  

AI Systems Are Best When: 

• Large datasets are available  

• Patterns are complex or unknown  

• Prediction or optimization is needed  

• Continuous improvement adds value   

In many cases, a hybrid approach is optimal, combining rule-based controls with AI-driven insights. 

Conclusion 

Expert systems laid the foundation for digital decision-making in regulated environments by bringing structure, consistency, and a high level of compliance through rule-based logic. Today, AI systems are extending that foundation by introducing adaptability, predictive capabilities, and new efficiencies that enable organizations to move beyond static processes toward more intelligent, data-driven operations. However, this evolution also brings increased responsibility, as AI requires stronger governance, more sophisticated validation frameworks, and a clear understanding of associated risks such as bias, model drift, and lack of transparency. For organizations operating in GxP environments, success will depend on balancing innovation with control, leveraging AI to enhance human expertise while maintaining compliance, traceability, and trust.  

At InnovX, we see the future of GxP not just as digital, but as intelligently compliant, where advanced technologies are integrated thoughtfully within a robust quality framework. 

References 

[1] Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson. 

[2] European Medicines Agency (EMA). (2023). Reflection paper on the use of AI in the medicinal product lifecycle.