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New machine learning tools show promise but need better testing for diverse patients with diabetes

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New machine learning tools show promise but need better testing for diverse patients with diabetes
Photo by Marek Studzinski / Unsplash

A recent look at computer programs used to predict heart problems in patients with type 2 diabetes shows some exciting potential. These new systems, which use artificial intelligence, performed better than older ways of guessing who might have heart trouble. They were checked against patient data to see how well they could tell the difference between safe and risky cases.

However, there are serious problems with how these tools were made. Many of them have a high chance of being wrong because of errors in the data or the way they were built. This makes it hard to trust their results completely without more careful checking.

Another big issue is who was studied. Most of these programs were created using data from people living in Europe and North America. This means they might not work well for patients from Asia or other parts of the world. Doctors need to make sure these tools are fair for everyone before using them in real life.

The main lesson is that while these new technologies are promising, we must fix their flaws first. We need to test them on more diverse groups of people and make sure they are built correctly. Only then can doctors safely use them to help patients with diabetes avoid heart attacks.

What this means for you:
AI tools predict heart risk better but need testing on diverse groups and fixing errors before doctors use them.
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