Should We Use AI for Medical Advice? It's Complicated - ThrowForward Thursday 187

Speaker A: Should you use generative AI for medical advice? The short answer is no, although the medium answer is yes if you are already a doctor. The longer answer is this. My name is Graham Codrington. This is Throw Forward Thursday AI as a medical doctor, yes or no? The Reasoning With Machines Laboratory, located at the University of Oxford in the uk, uh, did a remarkable piece of research earlier this year. Uh, there are links in the notes if you're interested in reading up more about it. They put together a number of scenarios, um, scenarios, uh, of people who might be having medical problems, some of them minor, some of them incredibly serious, with the need to call an ambulance immediately. And they took these scenarios firstly to a group of about a hundred medical professionals and asked the medical professionals to put the symptoms in. It's things like, um, I've got a striking headache, I'm losing vision in my left eye, my arm is tingling, that sort of thing. Uh, and then ask the system any one of the various, uh, large language models, uh, and generative AI platforms. Ask them, you know, give me a diagnosis, tell me what to do. When those hundred medical professionals put that information into these systems, they got about a, uh, 95% accuracy rate. And 95% would be good enough to say we can use these systems for medical assistance. But, but that's not the end of the story because they then took the exact same scenarios to 1,300 randomly chosen normal people without medical training and asked them to do the same thing. And there the accuracy and validity of the responses of these AI platforms plummeted to only about a third 34% accuracy, which is a disaster. Um, and basically was medically, not, I want to say medically problematic, but it was actually dangerous. And here I think you can work out, uh, what we should be doing, what the answer is to should we use AI for medical advice. The answer is, if you are already a medical professional with medical expertise, you will have the language of, of medicine. You. Instead of saying, my vision is going a bit blurry, I have a headache, you might be able to say, the vision in my left eye has begun to blur at the edges. I have a headache that is pounding in the back of my head, towards the top of my neck at vertebra number this or that, and you would describe the tingling in your arm in a very particular way because you already have a base level of knowledge and an of the language that you might use. Tingling, for example, might not be the right word. What happens is the large language model, which probably does have the right answer. The way you prompt it will enable it to get to a better answer for you and you will also be able to push back and have some level of filter to the first response to be able to ask an additional question or realize it didn't quite understand what you had said, give it a bit more information and get to 95%. In the hands of somebody uh, who doesn't have that expert level knowledge already, it becomes a disaster. This is a wonderful example of the Dunning Kruger effect. I'm sure you know this. The effect of knowing that when you don't know what you don't know, it's very easy to think that you are an expert. As soon as you begin to learn something uh, about a topic, you realize how little you know and you underestimate your ability until you develop a little bit more expert knowledge and then you've got uh, a little bit more confidence but also a healthy dose of humility at the same time. The problem with large language models is they are designed to give very confident, very specific, very authoritative sounding responses even when they are not actually um, as clear and authoritative and factual as they should be. And the bottom line is large language models have no way of self checking themselves. They have no way of uh, applying the Dunning Kruger effect filter to themselves. That's a human thing that we can do in large language models. Literally can't even be programmed to do that yet. Maybe the future, but for now, not the future for now. Uh, if you have expertise in a field, large language models become a fantastic tool to improve and increase your expertise. If you are not an expert, they can be dangerous. Our team at Tomorrow Today has built a model that we call the 5T AI, uh, impact model that helps organizations to think about where and when AI can be best used and how to get the best out of AI. The five T's refer to five different levels at which AI can uh, be implemented very, very simply. Uh, the first level is about productivity, personal uh, productivity gains with uh, automating, um, and improving your tasks. The second is when AI becomes a team member. This is when you, who already have expertise, uh, allow AI, uh, to be a tool as part of your team to improve and increase your expertise. The third level is by the way where most businesses will see real bottom line profit improvements. This is why most companies are not seeing it. Because this together level, um, or sometimes called the transversal level, is where you look for workflow redesign and we believe that that's where you start to see the real value. And we also know that about 95% of our clients haven't even got there yet. They still at these first two levels, maybe even only the first level. The fourth level is what everybody wants, which is transformative innovation. But unless you've built the foundation of these other three things, you're not going to get that fourth level. And the fifth level is more of a pillar that runs throughout this and that is we have to trust the systems. And that comes back, uh, to that Oxford University research that we can only trust the system if we know the sorts of things the system should be producing. AI is never going to replace us as humans. AI is going to augment existing human expertise supercharged and make us bionic in what we do. That is how you really get impact. Measurable, bottom line impact from AI. Slightly different in the throw Forward Thursday studio this week. Maybe more of a PSA this week than a future focus. But if you'd like to know more about our 5T model and if you'd like to know more about how to unlock real AI value, getting beyond the hype, make sure you contact our team for a discovery call. We'd love to chat to you and take you into tomorrow's world today. I'll see you next week in our studio.