“We have reached a point where AI is teaching us”
“This subject engages me deeply,” says Johan Sundström, cardiologist and Professor of Epidemiology, and one of the organisers of the programme of the Olof Rudbeck Day 2024. Foto: Mikael Wallerstedt.
Over the past year, AI has been a hot topic at many breakfast tables and in many lunchrooms, thanks to the explosive development of so-called generative AI. This year’s Olof Rudbeck Day will address the same subject, but in relation to what AI can mean for healthcare and people’s health.
On Friday, 18 October, the annual Olof Rudbeck Day will take place, with the aim of disseminating information about current medical research on a specific theme. This year’s topic is ‘AI for health and care – opportunities and risks.’
“This subject engages me deeply. There are significant challenges in healthcare today, and AI comes with a promise to solve many of them. That said, it is not a solution to be taken lightly; we must collectively understand the most likely paths for AI in healthcare in the short and long term,” says Johan Sundström, cardiologist and Professor of Epidemiology, and one of the organisers of the programme.
Johan and his research group are running several projects involving AI as a tool in healthcare, and he is part of the interdisciplinary initiative AI4Research. He looks forward to a busy event in October.
“I think it’s fantastic to enjoy all of this in one day. There are so many aspects of AI, both good and bad, that need to be discussed, and this is a perfect forum.”
Finding patterns in ECG
AI models and algorithms can be essential tools in many areas of healthcare, but perhaps less suitable in others. In image interpretation, which is the focus of one of Johan Sundström’s research projects, significant progress has already been made.
“There is a lot of research going on in AI for image interpretation and pattern recognition, and it is primarily in this area that I see the greatest potential right now,” says Johan.
When an AI model is trained on a large number of human interpretations of ECGs, the AI will eventually become as skilled as a human in interpreting them, reaching a ‘human’ level of expertise. Johan Sundström’s research group has taken this a step further.
“In addition, we have trained the model to recognise other patterns by allowing the AI to learn from patient outcomes, whose ECGs were previously interpreted. For instance, one patient needed a coronary angioplasty after their acute myocardial infarction, while another did not. Ultimately, the AI model learns to recognise patterns regarding which interventions a patient might need. This means it can do things that we humans cannot. Suddenly, it seems that the AI is ‘superhuman’, and it starts teaching us how it detected the patterns by highlighting where on the ECG curve it found something.”
Text: Robert Widing
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