Decoding GPT Accuracy: Insights into AI in Healthcare

Misconceptions about AI language models' capabilities are clarified, with solutions suggested for improving their accuracy.

A study reveals over 98% of cited articles by OpenAI model, GPT-3.5, and around 20.6% by GPT-4 were fake. However, there is a fundamental flaw in with this study and it is in it's assumption on how these language models work under the hood. Although I do agree that we must always verify the information generated from the models is correct, I do believe it is helpful to understand how they generate their responses so that we as healthcare providers and educators can learn to more appropriately integrate this technology in our practice.

These models, including GPT-4 and GPT-3.5, are not designed to pull data from specific databases or individual studies. Instead, they work more like a creative writer who has read a vast range of texts and generates new content based on patterns they've seen before. Imagine a highly skilled linguist who has read thousands of books but can't recall specific passages from individual texts.

Instead, they can generate new sentences based on the patterns, structures, and information they've absorbed. This is how these language models work. They operate in what we call an "embedding space," a kind of mental map where related ideas are grouped together. When asked a question, they don't recall a specific text, but generate an answer based on this mental map.

For this reason, expecting these models to cite specific studies accurately is a bit like asking our linguist to remember a specific passage from one of their thousands of books. The high error rates observed in your study might be due to this fundamental aspect of how the models operate.

In addition to understanding how these language models work, it's also crucial to be aware of techniques that can help mitigate some of the inaccuracies or 'hallucinations' these models might produce. Techniques such as "grounding" and "retrieval-augmented generation" can be particularly beneficial.

For example, Grounding involves connecting the generated text to real-world knowledge or a trusted data source, thereby anchoring the machine's responses in reality. In other words, it helps to tie the model's output to something concrete and verifiable. This technique can help ensure that the information the model provides is accurate and reliable.

On the other hand, retrieval-augmented generation is a technique where the model is first supplied with a set of relevant documents from which to generate its responses, instead of relying solely on the patterns it has previously learned. This can help ensure that the model's responses are more directly linked to existing credible sources of information.

Both of these techniques can be useful tools for healthcare professionals who want to leverage AI technology in their practice. By integrating these methods with AI, they can generate more accurate and credible responses, thereby improving the overall effectiveness and reliability of the AI system in healthcare settings. I believe it is important for healthcare professionals to be aware of these techniques and consider their application when planning to integrate AI in their practice.

Nevertheless, the study importantly highlights the need for manual verification and fact-checking, especially in sensitive fields like healthcare and education.