Generative AI systems become increasingly integrated into healthcare and everyday decision-making, so the challenge of understanding how these models function has gained urgency. Large language models (LLMs) such as ChatGPT are powerful but often operate as “black boxes”, leaving clinicians wary of trusting their outputs. Concerns about AI-generated inaccuracies, or hallucinations, further complicate adoption in safety-critical fields. Mechanistic interpretability, a growing area of research, seeks to shed light on these inner workings and improve confidence in LLMs through deeper scrutiny.
Understanding the Mechanics Behind Language Models
Large language models function very differently from traditional reference tools like textbooks or databases. Instead of retrieving verified facts, they generate responses by predicting likely sequences of words based on patterns in vast datasets. This statistical pattern matching allows them to appear knowledgeable, but it also means they lack an inherent understanding of logic, truth or the limits of their own knowledge. Consequently, LLMs may fabricate responses that sound plausible but are inaccurate, especially if users are unclear or overly general in their queries.
At a technical level, LLMs rely on transformer architecture and attention mechanisms to process user inputs. The process begins by converting a user’s prompt into numbers—known as tokens—which are then transformed into vectors representing meaning. Positional encoding ensures that the sequence of words is preserved, which is crucial for the model to interpret context correctly. The model then uses its training data to predict the next most likely word in the sequence. While these processes are well documented in foundational AI research, they do not fully explain how language models handle more abstract concepts or reasoned responses.
Looking Inside: Interpreting Model Reasoning
To gain deeper insights, AI researchers have been developing tools and methods to observe what happens within these models as they generate answers. Simply examining neuron activations—the basic units of computation in neural networks—has proven insufficient. This is because individual neurons do not represent individual concepts in isolation. Instead, concepts are distributed across many neurons, and each neuron contributes to multiple concepts. As a result, trying to understand model reasoning by inspecting individual components is akin to trying to understand a novel by looking at one letter at a time.
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Google DeepMind and other institutions have turned to mechanistic interpretability as a way forward. The aim is to uncover the higher-order features that represent more complex ideas. Tools like Gemma Scope, developed by DeepMind, use sparse autoencoders to probe these networks more effectively. These autoencoders act like digital microscopes, enabling researchers to isolate and examine layers of data that represent broad concepts. By systematically categorising data features and tracing how they influence outputs, researchers hope to determine whether models are reasoning accurately—or deceptively.
Mitigating Risk While Research Progresses
Despite advances in interpretability, the technology is still far from fully understood, particularly in the clinical context where trust and accuracy are paramount. For this reason, practical strategies are essential when deploying LLMs in healthcare and other sensitive areas. One approach is to use chatbots that are specifically trained on clinically vetted datasets or those that include retrieval-augmented generation (RAG) capabilities. These systems enhance their responses by sourcing content from reliable, ground-truth material, reducing the likelihood of hallucinated outputs.
Users can also take steps to improve the quality of AI-generated answers. Precision and detail in prompts are critical, as vague or generalised queries increase the risk of misleading responses. Requesting peer-reviewed references and verifying them independently can further reduce error. Providing contextual information, such as patient history or lab results, enables the model to tailor its response more effectively. Additionally, refining the question based on the initial output helps clarify ambiguity and guide the model toward more accurate answers. These practices form the basis of responsible prompt engineering, a skill that is increasingly necessary for safe interaction with generative AI.
While full transparency into the inner workings of generative AI remains elusive, ongoing research into mechanistic interpretability offers a promising path toward greater understanding. In the meantime, thoughtful use and cautious evaluation of LLM outputs are essential, especially in healthcare. By combining emerging insights from AI research with responsible user practices, it is possible to benefit from these technologies while mitigating their risks. The goal is not only to use generative AI effectively but to ensure its outputs align with the standards of truth, safety and utility demanded in professional settings.
Source: Mayo Clinic
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