Because existing validation frameworks may be sufficient for some novel technologies but not for others, there is a need for a classification of intelligent automation systems and the best-suited validation framework for each. Table 1 provides a breakdown of the types of automated pharmacovigilance (sub-) systems, some of the underlying technologies used within them, and examples of their application to pharmacovigilance processes.
In the classification (Table 1), the term “artificial intelligence” or AI is used to describe the simulation of human intelligence processes by computer systems but excludes purely rule-based systems. AI encompasses a wide range of technologies, including ML, text recognition, NLP, and machine translation. The considerations and elements described in this paper apply to supervised learning systems, within which the training data are curated and known by the model developers and subject matter experts .
For each classification of AI systems, the availability of a validation framework is categorized as established, emergent, or eventual (Table 1). “Established” frameworks are those for which clear guidance exists to support validation efforts. With “emergent” frameworks, a foundation exists but is insufficient for demonstrating fit-for-use systems within today's regulated environment. No validation framework yet exists for “eventual” systems. The focus of our research is to propose an extended validation system for AI-based static systems.
We based our classification of automated pharmacovigilance systems in Table 1 on the main ways people typically interact with data. The primary purpose was to identify considerations for validation of automation systems. We recognize this is an evolving area, and there may be a need to define additional classes in the future.
Validation of Artificial Intelligence (AI)-Based Systems Require Considerations Beyond Those Needed for Rule-Based Systems
Compared with rule-based systems, AI-based systems do not consist of a set of pre-defined rules but generally include models derived from a training data set (e.g., supervised ML based on "ground truth") or more generalized modeling of a “human-like” capability (e.g., auto-translation, NLP).
The potential advantages of AI-based systems are that more complex associations and patterns sometimes unknown to humans can be considered by a model, resulting in greater flexibility and ability to handle variable input (e.g., NLP for the handling of unstructured text).
However, the use of AI introduces some potential challenges related to the validation of these systems (Sect. 3.2).
Validation of AI-Based Static vs. AI-Based Dynamic Systems
Dynamic systems are those in which new data (e.g., newly received ICSRs) are used to continually update the model for future data, in contrast to static systems that use batch learning techniques that generate the model by learning on the entire training data set at once. A dynamic system is referred to as “online ML” or an “online adaptive system.”
Such systems will dynamically adapt to new patterns in data and should proactively improve over time; however, they also introduce potential risks in terms of stability and performance of the model over time.
As these systems are inherently dynamic in nature, current validation and change control guidelines are no longer adequate to demonstrate consistent and reliable system performance. Moreover, some AI algorithms do not learn in a deterministic way, which adds additional validation challenges when such algorithms are not restricted to a static state. This reality will again require decisions regarding any re-validation over time.
To date, electronic systems utilized in pharmacovigilance are almost exclusively static. Dynamic systems may be used more frequently in the future when appropriate methods for timely validation of these systems become available and if the benefits of using a dynamic system outweigh the potential risks associated with these systems.