Hswen Y, Abbasi J.
JAMA. Published online October 25, 2023.
Here is an excerpt:
Dr Bibbins-Domingo: When these types of generative AI tools first came into prominence or awareness, educators, whatever level of education they were involved with, had to scramble because their students were using them. They were figuring out how to put up the right types of guardrails, set the right types of rules. Are there rules or danger zones right now that you’re thinking about?
Dr Chang: Absolutely, and I think there’s quite a number of these. This is a focus that we’re embarking on right now because as exciting as the future is and as much potential as these generative AI tools have, there are also dangers and there are also concerns that we have to address.
One of them is helping our students, who like all of us are still new to this within the past year, understand the limitations of these tools. Now these tools are going to get better year after year after year, but right now they are still prone to hallucinations, or basically making up facts that aren’t really true and yet saying them with confidence. Our students need to recognize why it is that these tools might come up with those hallucinations to try to learn how to recognize them and to basically be on guard for the fact that just because ChatGPT is giving you a very confident answer, it doesn’t mean it’s the right answer. And in medicine of course, that’s very, very important. And so that’s one—just the accuracy and the validity of the content that comes out.
As I wrote about in my Viewpoint, the way that these tools work is basically a very fancy form of autocomplete, right? It is essentially using a probabilistic prediction of what the next word is going to be. And so there’s no separate validity or confirmation of the factual material, and that’s something that we need to make sure that our students understand.
The other thing is to address the fact that these tools may inherently be structurally biased. Now, why would that be? Well, as we know, ChatGPT and these other large language models [LLMs] are trained on the world’s internet, so to speak, right? They’re trained on the noncopyrighted corpus of material that’s out there on the web. And to the extent that that corpus of material was generated by human beings who in their postings and their writings exhibit bias in one way or the other, whether intentionally or not, that’s the corpus on which these LLMs are trained. So it only makes sense that when we use these tools, these tools are going to potentially exhibit evidence of bias. And so we need our students to be very aware of that. As we have worked to reduce the effects of systematic bias in our curriculum and in our clinical sphere, we need to recognize that as we introduce this new tool, this will be another potential source of bias.
Here is my summary:
Bernard Chang, the Dean for Medical Education at Harvard Medical School, argues that artificial intelligence (AI) is poised to transform medical education. AI has the potential to improve the way medical students learn and train, and that medical schools should not only embrace AI, but also take an active role in shaping its development and use.
Chang identifies several areas where AI could have a significant impact on medical education. First, AI could be used to personalize learning and provide students with more targeted feedback. For example, AI-powered tutors could help students learn complex medical concepts at their own pace, and AI-powered diagnostic tools could help students practice their clinical skills.
Second, AI could be used to automate tasks that are currently performed by human instructors, such as grading exams and providing feedback on student assignments. This would free up instructors to focus on more high-value activities, such as mentoring students and leading discussions.
Third, AI could be used to create new educational experiences that are not possible with traditional methods. For example, AI could be used to create virtual patients that students can interact with to practice their clinical skills. AI could also be used to develop simulations of complex medical procedures that students can practice in a safe environment.
Chang argues that medical schools have a responsibility to prepare students for the future of medicine, which will be increasingly reliant on AI. He writes that medical schools should teach students how to use AI effectively, and how to critically evaluate AI-generated information. Medical schools should also develop new curricula that take into account the potential impact of AI on medical practice.
