Complexity and Priorities - Explaining AI in Pharmacovigilance.

Complexity and Priorities - Explaining AI in Pharmacovigilance.
Understanding the relevance of AI and Large Language Models (LLMs) like GPT-4 within pharmacovigilance can be difficult, but the key is to demystify their complexity and prioritize explanation.

The challenge of complexity.

LLMs can be intricate with complex interactions using millions of parameters, but they are not a mystery because they follow clear, repeatable, rules and sequences.

They tokenize text into linguistic ‘units’, find connections and assign importance to them, consider context, and select best choices based on historical stats and other rules in a well-defined process. AI may seem “opaque”, but it is still just pattern-based learning performed by computers.

Beyond the "Black Box".

Although large LLMs like GPT-4 may be complex, the explanation of processes within “smaller” LLMs, can be done through various established ‘white’ and ‘black’ box techniques. These include Layer and Neuron Analysis, Attention Visualization, Feature Ablation Studies, Saliency Maps, LIME, SHAP, Counterfactual Explanations, Integrated Gradients, Model Simplification and Distillation and many others.

And since we can explain smaller models, we can also explain larger ones better.

Priorities and Explainable AI.

Full explanation is typically not a priority today because the focus is on results and it would need considerable resources to do so, but at this point, do we have the right mindset and business case for it anyway? Well in Pharmacovigilance we do, because not having the ability to fully explain the processes of LLMs is still a barrier for large scale industry adoption.

The good news is that Explainable AI (XAI) is on the rise, and this along with the obvious benefits for the large and complex data activities found in pharmacovigilance, is allowing an accelerating case and use of AI and LLMs.

But that also means that pharmacovigilance professionals need to be equipped NOW with the knowledge and understanding required to manage this rapid and inevitable change. Those who understand and embrace AI quickly will continue to be relevant and prosper.

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