AI is offering medical students real-time expert feedback amid an ‘ever-increasing provider shortage’.
The new tool, trained on videos of expert surgeons at work, offers students real-time personalised advice as they practice suturing.
Initial trials suggest AI can be a powerful substitute teacher for more experienced students.
This is according to Mathias Unberath, an expert in AI-assisted medicine who focuses on how people interact with AI.
He explained: ‘We’re at a pivotal time. The provider shortage is ever-increasing, and we need to find new ways to provide more and better opportunities for practice. Right now, an attending surgeon who is already short on time needs to come in to watch students practise, rate them, and give them detailed feedback – that just doesn't scale.
‘The next best thing might be our explainable AI that shows students how their work deviates from that of expert surgeons.’
Developed at Johns Hopkins University, the pioneering technology was showcased and honoured at the recent International Conference on Medical Image Computing and Computer Assisted Intervention.
Currently, many medical students watch videos of experts performing surgery and try to imitate what they see. There are even existing AI models that will rate students, but according to Unberath, they fall short because they don't tell students what they're doing right or wrong.
He said: ‘These models can tell you if you have high or low skill, but they struggle with telling you why. If we want to enable meaningful self-training, we need to help learners understand what they need to focus on and why.’
The team’s model incorporates what's known as ‘explainable AI’, an approach to AI that, for example, will rate how well a student closes a wound and then also tell them precisely how to improve.
The team trained their model by tracking the hand movements of expert surgeons as they closed incisions. When students try the same task, the AI texts them immediately to tell them how they compare to an expert and how to refine their technique.
Catalina Gomez, a Johns Hopkins PhD student in computer science, said: ‘Learners want someone to tell them objectively how they did. We can calculate their performance before and after the intervention and see if they are moving closer to expert practice.’
The team performed a first-of-its-kind study to see if students learned better from the AI or by watching videos. They randomly assigned 12 medical students with suturing experience to train with one of the two methods.
All participants practised closing an incision with stitches. Some got immediate AI feedback while others tried to compare what they did to a surgeon in a video. Then everyone tried suturing again.
Compared to students who watched videos, some students coached by AI, those with more experience, learned much faster.
Unberath said: ‘In some individuals, the AI feedback has a big effect. Beginner students still struggled with the task, but students with a solid foundation in surgery, who are at the point where they can incorporate the advice, found it had a great impact.’
The team now plans to refine the model to make it easier to use. They hope to eventually create a version that students could use at home.
