Large Language Models - Now We Are Talking
Oct 19, 2023
Large language models like ChatGPT showcase the remarkable natural language capabilities of AI, but also have key limitations we must recognize.
The core innovation enabling their human-like fluency is transformer neural networks, which can model the complex relationships between words. After pre-training on massive text datasets, fine-tuning specializes them for particular tasks.
The results are impressive - LLMs can generate remarkably coherent narratives on most topics. However, they lack true comprehension of the text they produce. Their knowledge is restricted to their training data.
So while LLMs can provide draft summaries of patient histories or clinical journal articles, for instance, physicians must thoroughly review these for accuracy. LLMs cannot replace clinical expertise and responsibility.
Major risks include conveying biased or false information if not carefully guided. As "black boxes", LLMs also cannot explain the reasoning behind their outputs. Caution is warranted.
But applied judiciously under human oversight, LLMs offer a powerful AI aid to knowledge workers. The key is understanding their strengths as productivity enhancers while mitigating their limitations through governance and supervision.
What are your thoughts on how LLMs like ChatGPT might assist clinicians without compromising care standards? I'm eager to have a balanced discussion on responsibly leveraging their capabilities.
